# Data Truffle > A seller-side agent that prices your company's operational data before it goes to market and brings back an offer from AI training buyers in 24 to 48 hours. Data Truffle helps established companies license their operational records to AI training buyers. We price the data before it goes to market, run a competitive process with vetted buyers, and return a first offer within 24 to 48 hours of a complete manifest. Success fee only. Your data never passes through us. ## Key facts - Offer window: first indicative offer in 24 to 48 hours of a complete manifest. Measured from a complete data manifest. A first offer is a process target, not a guarantee; buyers decide eligibility, price, and terms. - Fees: Success fee only, paid when you are paid. No upfront fee, no retainer, no listing fee. - Custody: Your datasets never pass through us. Any transfer happens directly between your company and the buyer, after you sign. - Qualification today: 20+ desk staff, 3+ years of records, data in 3+ systems. - Indicative market range for single-company operational data: $50K to $5M. Indicative range observed across the market for single-company operational data licenses. Not a quote. Actual value depends on buyer demand, data quality, rights, and exclusivity. ## Start here - [Get your offer](https://datatruffle.ai/get-offer): Answer seven questions about your company. If you qualify, we price your data and bring back a first offer in 24 to 48 hours. No dataset needed to start. - [What is your company's data worth?](https://datatruffle.ai/estimate): A 60-second indicative range for licensing your operational records to AI training buyers. No files, no login, nothing stored. - [How it works](https://datatruffle.ai/how-it-works): Six steps from a one-page manifest to a signed license: inventory, pricing, rights check, competing bids, your approval, direct transfer. First offer in 24 to 48 hours. - [Pricing — success fee only](https://datatruffle.ai/pricing): No upfront fee, no retainer, no listing fee. A seller-side success fee paid only when a license closes and you are paid. Full fee schedule, no surprises. - [Book a 20-minute fit call](https://datatruffle.ai/book-call): Talk to a human about whether your records are licensable, what they could be worth, and what to check before you sign anything. No pitch deck. ## Pages - [Data Truffle — Your data is worth more than you think](https://datatruffle.ai/): We price your company's operational data before it goes to market and bring back an offer from AI training buyers in 24 to 48 hours. Success fee only. - [How it works](https://datatruffle.ai/how-it-works): Six steps from a one-page manifest to a signed license: inventory, pricing, rights check, competing bids, your approval, direct transfer. First offer in 24 to 48 hours. - [The hunt: how we find the buyer who values your data most](https://datatruffle.ai/the-hunt): Why the same records are worth 3x to one lab and nothing to another, and how a seller-side agent prices the data before buyers ever see it. - [Pricing — success fee only](https://datatruffle.ai/pricing): No upfront fee, no retainer, no listing fee. A seller-side success fee paid only when a license closes and you are paid. Full fee schedule, no surprises. - [What is your company's data worth?](https://datatruffle.ai/estimate): A 60-second indicative range for licensing your operational records to AI training buyers. No files, no login, nothing stored. - [Get your offer](https://datatruffle.ai/get-offer): Answer seven questions about your company. If you qualify, we price your data and bring back a first offer in 24 to 48 hours. No dataset needed to start. - [Book a 20-minute fit call](https://datatruffle.ai/book-call): Talk to a human about whether your records are licensable, what they could be worth, and what to check before you sign anything. No pitch deck. - [Privacy & data custody](https://datatruffle.ai/privacy-and-custody): Your datasets never pass through us. The chain of custody, de-identification, and the contract terms that keep your customers' information out of any model. - [What sells: the data types AI labs pay for](https://datatruffle.ai/data-types): Email, documents, chat, calls, CRM, support tickets, billing and engineering records. Which ones carry value, why, and how they are prepared. - [Industries](https://datatruffle.ai/industries): Operational data from professional services, legal, accounting, healthcare administration, software, staffing, BPO, logistics, real estate and agencies. - [Who buys company data for AI training](https://datatruffle.ai/buyers): AI labs, data labs and model builders: what they are looking for, how they evaluate a dataset, and how the vetting standard works. - [FAQ](https://datatruffle.ai/faq): Every question owners ask before a first call: value, speed, custody, de-identification, fees, exclusivity, and what to check before signing. - [Glossary of AI data licensing terms](https://datatruffle.ai/glossary): Manifest, exclusivity, field of use, de-identification, provenance, refresh rights, and the other terms you will see in a data license. - [Guides](https://datatruffle.ai/guides): Plain-English guides to licensing company data to AI buyers: the basics, your rights, red flags, de-identification, and what drives value. - [Compare your options](https://datatruffle.ai/compare): Going direct to a lab, listing on a marketplace, or using a seller-side agent. What each costs you in price, time, and risk. - [Blog](https://datatruffle.ai/blog): Field notes on the AI training-data market: what buyers want, what deals look like, and how owners get paid. New posts nightly. - [About](https://datatruffle.ai/about): Why we built a seller-side agent for company data, who runs it, and the standard we hold buyers to. - [Contact](https://datatruffle.ai/contact): Questions about licensing your company's data, partnerships, or press. We answer within one business day. - [Privacy policy](https://datatruffle.ai/privacy): How this website handles the information you share with us. - [Terms of service](https://datatruffle.ai/terms): The terms that govern use of this website and our seller-side engagement. ## Data types buyers pay for - [Email and calendar: the decisions your company made in writing.](https://datatruffle.ai/data-types/email-and-calendar): Every client thread, internal approval, and scheduling exchange is a record of how work moved from request to resolution. Buyers want the sequence, not the sender. With people and companies replaced by consistent fictional values, years of mailbox history show a model how a real business communicates, decides, and follows through. - [Documents and SOPs: the way your company does the work, written down.](https://datatruffle.ai/data-types/documents-and-sops): Playbooks, templates, proposals, and checklists are the distilled version of years of trial and error. A model that reads them learns what a finished piece of work looks like and the steps that produce it. Buyers pay for the ones that are used, revised, and tied to real projects, not the ones written once and filed. - [Chat and collaboration: how your team solves problems while they are happening.](https://datatruffle.ai/data-types/chat-and-collaboration): Slack and Teams history is the closest thing to watching a company think. A question lands, three people weigh in, someone finds the answer, and the thread ends with a decision. Buyers want that back-and-forth because it is the part of work that never makes it into a document. - [Calls and transcripts: real conversations, with the ending attached.](https://datatruffle.ai/data-types/calls-and-transcripts): Recorded sales and support calls are the scarcest record type on this list. A transcript holds objections, hesitation, the moment a customer decides, and what the rep did next. Buyers building voice and conversation models want thousands of them, tagged with how each one turned out. - [CRM and sales workflows: every deal, and why it went the way it did.](https://datatruffle.ai/data-types/crm-and-sales): A CRM with years of history is a record of thousands of decisions by customers. Stage changes, call notes, lost reasons, and follow-up cadences show how a deal is actually worked. Buyers want the sequence from first touch to closed-won or closed-lost, with the notes that explain it. - [Support and ticket histories: issue, diagnosis, resolution, thousands of times over.](https://datatruffle.ai/data-types/support-and-tickets): A helpdesk is a machine for producing labelled examples. Each ticket starts with a problem described by someone who has it, continues with the questions an agent asks, and ends with a fix and a status. Buyers building support and agentic models want that loop, at volume, with the outcomes intact. - [Billing and operations: invoices, approvals, exceptions, and how work actually gets done.](https://datatruffle.ai/data-types/billing-and-operations): Operational systems record the unglamorous middle of a business: a job is scheduled, a part is ordered, an invoice goes out, a payment is late, someone decides what to do about it. Buyers value these records for the exceptions and approvals, not the ledger totals. - [Engineering workflows: triage, review, release, and incident records.](https://datatruffle.ai/data-types/engineering-workflows): Code is only part of what a software team produces. The issue that described the bug, the review comments that improved the fix, the release notes, and the incident timeline are the records that show how engineering decisions get made. Buyers building coding agents want that context around the code, not just the code. ## Industries - [Professional services firms: client onboarding, project delivery, and the methods you reuse.](https://datatruffle.ai/industries/professional-services): Consulting, engineering, architecture, and advisory firms produce the same records every engagement: a scoping conversation, a proposal, a plan, the delivery, and a review. Those sequences, repeated across hundreds of clients, are what buyers building business-task models want to learn from. - [Law firms and legal departments: matter workflows, drafting, review, and approval trails.](https://datatruffle.ai/industries/legal): Legal work is procedure made visible: intake, conflicts check, engagement, drafting, review, revision, filing, close. Buyers building legal-reasoning and document models pay a premium for records that show how experienced lawyers move a matter forward. The constraint is privilege, and it shapes everything about what can be licensed. - [Accounting and finance firms: reconciliation, close checklists, approvals, and client correspondence.](https://datatruffle.ai/industries/accounting-and-finance): Accounting firms and finance teams run the same disciplined cycle every month and every year: gather, reconcile, review, adjust, close, report. Buyers building finance and bookkeeping agents want to see that cycle performed by professionals, including the questions asked when the numbers do not tie. - [Healthcare administration: scheduling, billing, authorizations, and the non-clinical operations around care.](https://datatruffle.ai/industries/healthcare-administration): Practices, billing companies, and healthcare administrators run some of the most procedural workflows in any industry: eligibility checks, prior authorizations, claim submissions, denials, appeals, scheduling. Buyers want those administrative sequences. Clinical records are out of scope, and that line is the first thing we draw. - [Technology and software companies: issue triage, release workflows, and product documentation.](https://datatruffle.ai/industries/technology-and-software): Software companies generate the records buyers want most for coding and support agents: issues, pull requests, reviews, incidents, support tickets, and the documentation that connects them. The records are already structured, timestamped, and linked across systems. The question is usually rights, not relevance. - [Recruiting and staffing firms: sourcing, screening, placement, and client communication.](https://datatruffle.ai/industries/recruiting-and-staffing): A staffing firm runs a two-sided process thousands of times: understand a role, find candidates, screen them, present them, negotiate, place, follow up. Buyers want that sequence with the judgment in it, such as why a candidate was advanced or passed, and how a client's requirements shifted during the search. - [BPO and call centers: scripts, QA, resolution paths, and outcome-tagged calls.](https://datatruffle.ai/industries/bpo-and-call-centers): Outsourcers and call centers produce the highest-volume conversational records in business: calls, chats, and tickets, each handled under a script, scored by QA, and tagged with an outcome. Buyers building conversation and agent models want exactly that. The complication is that much of it is handled on behalf of clients, which puts the client's rights in the middle of the deal. - [Logistics and 3PL: dispatch, exception handling, scheduling, and the SOPs behind them.](https://datatruffle.ai/industries/logistics-and-3pl): Freight brokers, carriers, and warehouses run on exceptions: a late pickup, a damaged pallet, a reefer out of range, a driver who cannot make the appointment. The records of how dispatchers and account managers handle those moments, hour by hour, are what buyers building operations agents want to learn from. - [Real estate and property management: transaction coordination, leasing, and maintenance workflows.](https://datatruffle.ai/industries/real-estate-and-property): Brokerages and property managers coordinate many parties through long, document-heavy processes: listing, showing, offer, inspection, closing; application, lease, move-in, maintenance, renewal. The communication and checklists that move each one forward are what buyers want, with the people and addresses removed. - [Agencies and marketing teams: briefs, creative review, client approvals, and campaign operations.](https://datatruffle.ai/industries/agencies-and-marketing): Agencies turn a brief into finished work through rounds of review, and the rounds are the valuable part. The brief, the first draft, the client's comments, the revision, the approval, and the campaign report that followed show buyers how creative and marketing work is actually judged and improved. ## Guides - [Data licensing 101: how a company licenses its records to AI buyers](https://datatruffle.ai/guides/data-licensing-101): What a data license is and is not, who buys, what they pay for, how a deal moves from manifest to signature, and what you keep when it closes. - [The data rights checklist: what to confirm before you license anything](https://datatruffle.ai/guides/data-rights-checklist): Access is not permission. The ownership, contract, personal-data, regulatory, and platform questions to settle before records go in a manifest. - [The red-flags checklist: run it against anyone, including us](https://datatruffle.ai/guides/red-flags-checklist): Nine warning signs in a data-licensing approach, why each one matters, how we score against our own list, and what to do if you see one. - [De-identification, explained: what gets removed, what gets replaced, what stays](https://datatruffle.ai/guides/de-identification-explained): The three operations behind de-identification, a worked example, why consistency is the hard part, what the process cannot do, and what to put in the contract. - [What drives value: the factors that move a data license up or down the range](https://datatruffle.ai/guides/what-drives-value): There is no price list. Six factors that move an offer, what does not move it much, and how to find out where your records land before anyone names a number. ## Compare your options - [Going direct to one lab vs. a seller-side agent](https://datatruffle.ai/compare/going-direct): What changes when you take a buyer's call yourself instead of running a competitive process: price discovery, offers, speed, custody, terms, and fees. - [Listing on a data marketplace vs. a seller-side agent](https://datatruffle.ai/compare/marketplaces): What happens when you list a company's records on a self-serve data marketplace: who sets the price, who holds the data, who enforces de-identification, and what it costs to leave. - [Buyer-paid introduction routers vs. a seller-side agent](https://datatruffle.ai/compare/introduction-routers): Many services that offer to connect you with AI labs are paid by the buyer when you sign. What that means for price discovery, whose side they are on, and the terms you end up with. ## Blog - [What drives the value of a dataset](https://datatruffle.ai/blog/what-drives-the-value-of-a-dataset): Five things move the number more than anything else: years of history, volume, outcome labels, modality, and rights. How each one changes what a buyer will pay, and what you can do about it before going to market. - [Red flags when someone offers to buy your company's data](https://datatruffle.ai/blog/red-flags-when-someone-offers-to-buy-your-data): Inbound offers for operational data are landing in owners' inboxes. Most are legitimate. Some are not. Six signals that separate a real buyer or agent from a bad deal, and the question that exposes each one. - [De-identification, explained for owners](https://datatruffle.ai/blog/de-identification-explained-for-owners): Before a buyer trains on your records, the people in them are removed. Here is what that means in practice, what a de-identified ticket looks like next to the original, and what belongs in the contract. - [The case for competing offers](https://datatruffle.ai/blog/the-case-for-competing-offers): One buyer names one number. Several buyers reveal what the records are worth. A widely reported 2026 bankruptcy auction shows the gap, and the same logic applies to a company that is not for sale. - [What a one-page data manifest is, and why buyers price from it](https://datatruffle.ai/blog/what-a-one-page-data-manifest-is): A manifest is an inventory, not a dataset: what records exist, in which systems, over how many years, with what rights. It is the page buyers price from, and the reason you never send data to get a number. - [Why AI labs pay for operational records when the public web is exhausted](https://datatruffle.ai/blog/why-ai-labs-pay-for-operational-records): Models have read the public internet. What they have not seen is how a real company runs. That gap is why buyers now license operational records, and why yours may be worth more than you think. ## Optional - [Full content for AI agents](https://datatruffle.ai/llms-full.txt) - [RSS](https://datatruffle.ai/blog/rss.xml) - [Sitemap](https://datatruffle.ai/sitemap.xml) --- # Full content ## Data type: Email & calendar URL: https://datatruffle.ai/data-types/email-and-calendar Every client thread, internal approval, and scheduling exchange is a record of how work moved from request to resolution. Buyers want the sequence, not the sender. With people and companies replaced by consistent fictional values, years of mailbox history show a model how a real business communicates, decides, and follows through. Systems: Gmail, Outlook, Microsoft 365, Google Workspace Why buyers want it: - Threads show a request arriving, being routed, and being answered, with the reasoning in between. - Calendar records tie conversations to meetings, deadlines, and handoffs, which gives a model the shape of a working week. - Internal approvals and escalations in email are the judgment calls no manual captures. - Volume. A 40-person company sends and receives millions of messages over five years, and most of them are about work. What it contains: - Client threads from first inquiry to signed engagement, including replies, forwards, and the attachments referenced by name - Internal approval chains: a request, the questions asked, the sign-off - Scheduling exchanges and the calendar events they produced, with attendees reduced to roles - Vendor and supplier correspondence: quotes, revisions, confirmations - Escalations and complaint handling, with the resolution in the final message - Recurring internal updates: weekly reports, status notes, handoffs What drives value: - Years of continuous history in one domain. Five years from the same team is worth more than ten scattered mailboxes. - Thread completeness. Messages with the reply chain intact and the outcome visible outrank loose single messages. - Share of work-related mail. Buyers discount mailboxes dominated by newsletters and notifications. - Calendar linkage. Mail that can be matched to meetings and deadlines carries more workflow signal. Preparation: - Names, email addresses, phone numbers, and signature blocks are replaced with consistent fictional values, so a thread still reads as one conversation. - Personal mail, HR matters, legal threads, and anything flagged by your counsel are excluded by rule before any sample is built. - Confirm the mailboxes belong to the company (company domain, company accounts) and check client agreements for confidentiality clauses that cover correspondence. **Q: Do buyers read individual emails?** Buyers evaluate a small de-identified sample you approve, then train on the licensed copy as a whole. Nobody is reading your mail for its own sake. What the model learns is the pattern: how a request is phrased, who it goes to, what gets asked back, and how it closes. **Q: What about personal messages in work accounts?** They are excluded by rule. The preparation step filters out mail that is not about company business, along with HR, legal, and medical topics, before a sample exists. You review the rules and the sample before anything is licensed. **Q: Do we need every mailbox, or can we pick?** You pick. Most sellers include the shared and operational mailboxes (sales, support, projects, accounts) and a set of individual accounts whose owners agree. The manifest records which mailboxes and date ranges are in scope, and the license covers only those. ## Data type: Documents & SOPs URL: https://datatruffle.ai/data-types/documents-and-sops Playbooks, templates, proposals, and checklists are the distilled version of years of trial and error. A model that reads them learns what a finished piece of work looks like and the steps that produce it. Buyers pay for the ones that are used, revised, and tied to real projects, not the ones written once and filed. Systems: Google Drive, SharePoint, Notion, Confluence, Dropbox Why buyers want it: - SOPs are instructions a model can learn to follow, written by people who had to make them work. - Proposals and statements of work show how a problem is scoped, priced, and promised. - Revision history shows what changed and why, which is rarer than the final document. - A template next to its filled-in examples pairs a form with its real use. What it contains: - Standard operating procedures and runbooks, including their revisions - Proposals, statements of work, and scoping documents with the matching final deliverable - Checklists, QA forms, and audit trails with completed instances - Internal wiki and knowledge-base articles, with edit history where it exists - Onboarding guides, training materials, and policy documents - Meeting notes and decision logs linked to the projects they shaped What drives value: - Usage. A procedure that was followed 400 times outranks one that was written once and filed. - Pairing. A template next to twenty completed versions of it is a training set; the template alone is a form. - Revision history. Edits over the years show a process improving, which buyers value highly. - Specificity. Industry-specific methods carry more weight than generic office policies. Preparation: - Client names, pricing tied to a named account, and personal details inside documents are replaced or removed; the structure and the method stay. - Documents authored by third parties (vendor manuals, licensed templates, client-supplied materials) are excluded unless you hold the rights. - Confirm who owns work product created by contractors, and check client agreements for clauses that treat deliverables as the client's property. **Q: Our SOPs are out of date. Does that matter?** Less than you would think. Buyers value the history of a procedure as much as its current state, and a document that was revised six times is evidence of a process being improved. What matters more is whether the SOPs were actually used, which the completed checklists and the surrounding emails and chats show. **Q: Can we license templates without the client work that used them?** Yes, but the value drops. The template is the form; the completed instances are the examples a model learns from. If client-ownership clauses block the deliverables, the proposals, internal reviews, and process records around them are often still yours to include. **Q: What if a document contains a client's confidential information?** It is either de-identified so the client cannot be recognized from context, or excluded. The preparation rules you approve say which, and your counsel can require exclusion for any class of document, such as anything produced under a standalone NDA. ## Data type: Chat & collaboration URL: https://datatruffle.ai/data-types/chat-and-collaboration Slack and Teams history is the closest thing to watching a company think. A question lands, three people weigh in, someone finds the answer, and the thread ends with a decision. Buyers want that back-and-forth because it is the part of work that never makes it into a document. Systems: Slack, Microsoft Teams, Google Chat, Discord Why buyers want it: - Problem-to-fix threads show diagnosis in progress, not just the conclusion. - Channel history captures the handoffs, approvals, and quick decisions that happen between the formal records. - Threads, mentions, and reactions give a model structure to learn from: who was asked, who answered, what closed it. - It is continuous. A busy channel produces a dated, ordered record every working day for years. What it contains: - Project and client channels with the full thread history - Help channels where staff ask and answer internal questions - Incident and escalation threads from first report to all-clear - Approval exchanges: a request, the clarifying questions, the sign-off - Links to the documents and tickets each conversation produced - Standup and status updates posted on a schedule What drives value: - Signal density. Work channels outrank social channels, and buyers screen for the ratio. - Thread resolution. Conversations that end with a visible outcome carry the most value. - Years of continuous use on one platform, with the export intact. - Cross-links to tickets, documents, and CRM records that let a buyer follow one piece of work across systems. Preparation: - Usernames, display names, and mentions are mapped to consistent fictional handles so a thread still reads as one conversation. Direct messages are excluded by default. - Social, HR, and personal channels are dropped by rule, and anything your counsel flags is removed before a sample exists. - Check your workspace terms and any client agreements that cover shared channels. Messages from guest users may need to be excluded. **Q: Our chat is full of jokes and lunch orders. Is it still worth anything?** The work channels are. Preparation keeps project, client, support, and operations channels and drops the social ones. What buyers measure is the share of threads that end in a decision or a fix, and most companies with a few years on one platform have more of those than they expect. **Q: Are direct messages included?** Not by default. Direct messages are excluded unless both participants agree and your counsel is comfortable, and most sellers never include them. Channel history alone is usually what buyers are asking for. **Q: Does licensing chat history mean exporting our whole workspace?** No. The manifest names the channels and date ranges in scope. The export, when it happens, covers only those and goes directly to the buyer after you sign. We never receive it. ## Data type: Calls & transcripts URL: https://datatruffle.ai/data-types/calls-and-transcripts Recorded sales and support calls are the scarcest record type on this list. A transcript holds objections, hesitation, the moment a customer decides, and what the rep did next. Buyers building voice and conversation models want thousands of them, tagged with how each one turned out. Systems: Zoom, Gong, Fathom, Dialpad, RingCentral, Aircall Why buyers want it: - Spoken language is different from written. Models trained on text alone handle interruptions, hedging, and repair badly. - Outcome tags such as closed, escalated, refunded, or booked turn a transcript into a labelled example. - Multi-turn structure: a question, a clarifying question, an answer, a confirmation. - Domain vocabulary spoken by people who use it every day. What it contains: - Sales calls with the CRM outcome attached - Support and service calls with the ticket they produced - Call summaries and notes written by the rep afterwards - QA scorecards and coaching notes where the team keeps them - Scheduling and intake calls with the appointment record - Audio alongside transcripts, where consent and rights allow What drives value: - Volume with outcomes. Ten thousand calls tagged with results outrank fifty thousand untagged. - Transcript quality. Diarized, timestamped transcripts from a modern recorder are worth more than raw audio. - Consent documentation. Calls recorded under a clear notice are licensable; the rest are not. - Consistency. One phone system across several years keeps the format uniform. Preparation: - Names, phone numbers, addresses, card numbers, and account identifiers spoken on a call are redacted in both transcript and audio. Payment-card segments are removed outright. - Calls involving minors, medical detail, or legal matters are excluded by rule, along with anything your counsel flags. - Confirm your recording notice and consent practice meets the rules in every state or country your callers are in. Audio recorded under a single-party notice may be limited to transcript-only licensing. **Q: We recorded calls for quality purposes. Can we license them?** Often, but it depends on the notice callers heard and the law where they were. A clear recording notice usually supports licensing of de-identified transcripts. Audio is more sensitive, and some sellers license transcripts only. Your counsel reviews the consent practice before anything goes in the manifest. **Q: Do voices count as personal information?** Treat them as if they do. A voice can identify a person, which is why many deals cover transcripts rather than audio, and why audio deals specify voice handling in the contract. Names, numbers, and addresses are redacted in either case. **Q: We only have a few thousand calls. Is that enough?** A few thousand calls with outcomes attached is a real dataset, especially in a specialist domain. Volume matters, but outcome tags and transcript quality matter more. The manifest records the count, the years, and the tagging, and buyers price from that. ## Data type: CRM & sales workflows URL: https://datatruffle.ai/data-types/crm-and-sales A CRM with years of history is a record of thousands of decisions by customers. Stage changes, call notes, lost reasons, and follow-up cadences show how a deal is actually worked. Buyers want the sequence from first touch to closed-won or closed-lost, with the notes that explain it. Systems: Salesforce, HubSpot, Pipedrive, Zoho CRM, Close Why buyers want it: - Deal timelines show a process unfolding step by step, with dates. - Notes and lost reasons are the explanation a model cannot infer from the stage alone. - Won and lost deals together are a labelled set; one without the other is half a lesson. - Email and call activity logged against a deal links communication to outcome. What it contains: - Opportunity records with every stage change and its date - Contact and account notes written by reps after calls and meetings - Closed-lost reasons and competitor fields - Quotes, proposals, and the version that closed - Sequences and cadences: which touches happened, in what order, with what response - Pipeline reviews and forecast notes where the team keeps them What drives value: - Note quality. Reps who write two sentences after each call produce a far more valuable record than reps who log a single word. - Complete outcomes. A deal that ends in closed-won or closed-lost with a reason is worth more than an open one. - History in one CRM. Migrations lose stage history; five years on one system keeps it. - Linked activity. Deals with emails, calls, and meetings attached show the whole pursuit. Preparation: - Contact names, company names, emails, phone numbers, and addresses are replaced with consistent fictional values, so an account still reads as one account across all its deals. - Pricing tied to a named customer is generalized or bracketed. Competitor names can be kept or generalized at your choice. - Confirm your CRM terms permit export and licensing, and check customer agreements for confidentiality clauses that cover commercial terms. **Q: Our reps barely write notes. Is the CRM still useful?** Stage history with dates is still a workflow record, and the emails and calls logged against deals often carry the explanation the notes lack. The value is lower than a CRM with rich notes, and the manifest will say so, but it is rarely zero. **Q: Will buyers see our customer list?** No. Account and contact names are replaced with consistent fictional values before any sample is built. A buyer sees that Account 041 bought twice and churned once; they do not see who Account 041 is. **Q: Can we exclude certain accounts or deals?** Yes. You can exclude by account, by segment, by date range, or by any rule your counsel sets, such as every customer under a standalone NDA. The manifest records the scope and the license covers only that. ## Data type: Support & ticket histories URL: https://datatruffle.ai/data-types/support-and-tickets A helpdesk is a machine for producing labelled examples. Each ticket starts with a problem described by someone who has it, continues with the questions an agent asks, and ends with a fix and a status. Buyers building support and agentic models want that loop, at volume, with the outcomes intact. Systems: Zendesk, Freshdesk, Intercom, ServiceNow, Jira Service Management Why buyers want it: - The structure is exactly what a model needs: problem, investigation, resolution, each with a timestamp. - Tickets carry tags, priorities, and categories that act as free labels. - Internal notes show the reasoning agents keep out of the customer-facing reply. - Escalations and reopens show what did not work, which is as useful as what did. What it contains: - Full ticket threads: customer messages, agent replies, internal notes - Status and priority changes with timestamps - Macros and canned responses, with the tickets where they were used - Knowledge-base articles linked from tickets - Satisfaction ratings and reopen flags - Escalation paths to engineering, billing, or a supervisor What drives value: - Resolved share. A queue where 90% of tickets end in a documented fix is worth more than a larger queue of unresolved ones. - Years on one helpdesk. Migrations drop internal notes; continuity keeps them. - Category depth. Fifty distinct issue types outrank five. - Linked systems. Tickets that reference orders, accounts, or code changes let a buyer follow the fix across systems. Preparation: - Requester names, emails, phone numbers, addresses, and account IDs are replaced with consistent fictional values. Attachments containing personal data are removed. - Tickets involving payment-card data, health information, or minors are excluded by rule, along with anything your counsel flags. - Confirm your helpdesk terms allow export and licensing, and check customer contracts for confidentiality terms that cover support interactions. **Q: Our tickets contain customer account details. How is that handled?** Account identifiers, names, and contact details are replaced with consistent fictional values so a requester's five tickets still read as one requester. Attachments with personal data are dropped. Tickets in excluded classes, such as payment-card or health matters, never reach a sample. **Q: Do internal notes get licensed too?** Usually, and they are often the most valuable part, because they show the agent's reasoning. Internal notes go through the same de-identification as the customer-facing thread, and your counsel can exclude any class of note. **Q: Our helpdesk migrated two years ago. Did we lose value?** Some. Migrations often drop internal notes and status history, so the older tickets may be thinner. The manifest records what survived, and buyers price the two periods accordingly. Two years of complete history on the current system is still a real asset. ## Data type: Billing & operations URL: https://datatruffle.ai/data-types/billing-and-operations Operational systems record the unglamorous middle of a business: a job is scheduled, a part is ordered, an invoice goes out, a payment is late, someone decides what to do about it. Buyers value these records for the exceptions and approvals, not the ledger totals. Systems: QuickBooks, NetSuite, Xero, ServiceTitan, Jobber, Monday.com Why buyers want it: - Exception handling, such as a disputed invoice, a failed delivery, or a rush order, shows judgment under constraint. - Approval chains show who can decide what, and the thresholds that trigger a second signature. - Job and work-order records link a request to the steps and people that fulfilled it. - The data is structured and dated, which makes it easy to pair with the emails and chats around it. What it contains: - Invoices, credit notes, and the correspondence about them - Purchase orders and approval workflows with their thresholds - Work orders, job tickets, and dispatch records from first call to sign-off - Collections and dunning sequences with outcomes - Month-end close checklists and reconciliation notes - Vendor onboarding and exception logs What drives value: - Exception rate with resolution. The interesting records are the ones that did not go to plan and show how it was fixed. - Linkage to communication. An invoice dispute next to the email thread about it is worth more than either alone. - Years on one system. Continuity of job or invoice numbering keeps the history coherent. - Workflow depth. Multi-step approvals outrank one-click ones. Preparation: - Customer and vendor names, tax IDs, bank details, and addresses are replaced with consistent fictional values. Totals can be kept, bracketed, or scaled at your choice. - Payment-card numbers and bank account numbers are removed outright, which keeps PCI-scoped data out of any sample. - Confirm customer and vendor contracts do not treat pricing or terms as confidential in a way that blocks licensing, and check your accounting software's terms for export rights. **Q: Do buyers want our financial statements?** No. Buyers are not interested in your P&L. They want the workflow around transactions: how an invoice is raised, approved, disputed, collected, and closed. Totals can be scaled or bracketed without losing that. **Q: Is any of this subject to PCI rules?** Card numbers are. They are removed outright, along with bank account numbers, before a sample is built, so no PCI-scoped data is ever in the licensed copy. If your systems store full card numbers, that is worth fixing regardless of licensing. **Q: Our operations run across three systems that do not talk to each other. Is that a problem?** It is normal, and it is part of what buyers want to see: a work order in one system, the approval in email, the invoice in accounting. The manifest describes each system and the identifiers that connect them, and buyers value sets where a job can be followed across all three. ## Data type: Engineering workflows URL: https://datatruffle.ai/data-types/engineering-workflows Code is only part of what a software team produces. The issue that described the bug, the review comments that improved the fix, the release notes, and the incident timeline are the records that show how engineering decisions get made. Buyers building coding agents want that context around the code, not just the code. Systems: GitHub, GitLab, Jira, Linear, PagerDuty Why buyers want it: - Issue-to-merge trajectories show a problem being understood and solved, with the dead ends included. - Code review comments are expert feedback, in writing, on real changes. - Incident timelines and postmortems show diagnosis under pressure and the fix that followed. - Release and deployment records tie changes to outcomes. What it contains: - Issues and bug reports with triage labels and comment threads - Pull requests with review comments, requested changes, and approvals - Commit messages and the diffs they describe, where source rights allow - Incident channels, pager alerts, and postmortem documents - Release notes, changelogs, and deployment logs - Design docs and RFCs with their discussion threads What drives value: - Review depth. Teams that leave substantive comments produce far more valuable records than rubber-stamp approvals. - Linkage. Issue, pull request, commit, and incident records that reference each other let a buyer follow one change end to end. - Years in one tracker and one repository host, with history intact. - Rights clarity. Proprietary code you own outright is licensable; forks of open-source projects and client-owned code are not. Preparation: - Developer names, emails, and handles are mapped to consistent fictional identities. Secrets, keys, and credentials are scanned for and removed. - Client-owned repositories, code under restrictive open-source licenses, and anything your counsel flags are excluded by rule. - Confirm contractor agreements assign IP to your company, and check customer contracts for clauses that treat custom code as the customer's property. **Q: Does licensing engineering records mean licensing our source code?** Not necessarily. Many deals cover issues, reviews, incidents, and release records with the code excluded or limited to the diffs under review. Where code is included, it is only code you own outright, and the license defines the field of use. You decide where the line sits. **Q: What about secrets that ended up in old commits or tickets?** Preparation includes a secrets scan across commits, tickets, and chat, and anything found is removed before a sample exists. If a scan finds live credentials, rotate them regardless of the deal. **Q: We use open-source libraries everywhere. Does that block a deal?** Using open-source libraries does not. Licensing someone else's open-source code as if it were yours would. Your own repositories, with third-party dependencies excluded, are what goes in the manifest, and your counsel confirms the ownership of each one. ## Industry: Professional services URL: https://datatruffle.ai/industries/professional-services Consulting, engineering, architecture, and advisory firms produce the same records every engagement: a scoping conversation, a proposal, a plan, the delivery, and a review. Those sequences, repeated across hundreds of clients, are what buyers building business-task models want to learn from. What sells: - Proposal-to-engagement trails: the brief, the scoping questions, the statement of work, the kickoff plan - Project plans with the status updates, change requests, and client approvals that shaped them - Methodology documents next to the deliverables that applied them - Client onboarding checklists with their completed instances - Timesheet and utilization records tied to project outcomes - Post-engagement reviews and lessons-learned notes Workflows buyers value: - Scoping a vague request into a priced, bounded piece of work - Managing scope change: the request, the pushback, the revised agreement - Handing work between partner, manager, and analyst with a review at each step - Closing an engagement: final review, invoicing, feedback Sensitivities: - Client confidentiality. Most engagement letters restrict disclosure of client information. Licensing requires removing client identity and anything that could identify a client from context. - Work product ownership. Many contracts assign deliverables to the client. Your methods, templates, and internal process records are usually yours; the delivered report may not be. - Standalone NDAs. Projects under a separate non-disclosure agreement are excluded unless the counterparty consents. - Personal data of client staff appears throughout correspondence and is de-identified consistently. Professional-services firms tend to land in the middle of the range: the records are rich in method and decision, but client-ownership clauses often reduce how much of the delivered work can be included. **Q: Our deliverables belong to our clients. What is left to license?** Usually quite a lot. The proposal, the scoping emails, the project plan, the internal reviews, the methodology, and the timesheets are typically the firm's own records. The manifest separates what you own from what the client owns, and only the first category goes to market. **Q: Will a buyer be able to tell which clients we served?** Not from the licensed copy. Client names, staff names, and identifying details are replaced with consistent fictional values, and documents that would identify a client from context are generalized or excluded. You review the sample before anything is licensed. **Q: We are a 25-person firm. Is that big enough?** Twenty-plus desk staff with three or more years of records in three or more systems is the current threshold, so yes. Value depends more on the depth of your records than on headcount: a 25-person firm with eight years on one project tool and one CRM is a stronger set than a 60-person firm that migrated systems twice. ## Industry: Legal URL: https://datatruffle.ai/industries/legal Legal work is procedure made visible: intake, conflicts check, engagement, drafting, review, revision, filing, close. Buyers building legal-reasoning and document models pay a premium for records that show how experienced lawyers move a matter forward. The constraint is privilege, and it shapes everything about what can be licensed. What sells: - Drafting histories for your own precedent documents: template, first draft, redlines, final - Intake and matter-opening workflows: questionnaires, conflict checks, engagement letters - Internal review and approval trails on documents the firm authored - Playbooks, clause libraries, and negotiation position guides - Time entries and matter timelines that show the sequence of work - Non-privileged client correspondence about scheduling, status, and billing Workflows buyers value: - Turning a client's description of a problem into a scoped matter - Negotiating a contract: positions, fallbacks, and the clause that resolved it - Supervising associate work: comments, revisions, sign-off - Closing a matter: final deliverable, billing, file retention Sensitivities: - Attorney-client privilege and work product. Privileged communications and work product are excluded as a class, not de-identified. Your own precedent documents, playbooks, and internal process records are the licensable core. - Professional conduct rules on confidentiality go further than privilege. Most bars require client consent or complete removal of information relating to a representation. Review by your own counsel is a condition of going to market, not an option. - Court filings are public once filed, but the drafts and the communications around them are not. - Ethical walls. Matters subject to a wall need a per-matter decision before they appear in any manifest. Legal records tend to land toward the upper part of the range because buyers pay a premium for expert reasoning, but the privilege and confidentiality screen narrows what can be included. Firms that keep rich precedent libraries and documented internal process do best. **Q: Can a law firm license anything at all without breaching privilege?** Yes, because privilege attaches to client communications and work product, not to the firm's own precedents, playbooks, templates, training materials, and process records. Those are usually the most valuable records anyway. The manifest excludes privileged classes outright, and your ethics counsel reviews the scope before anything goes to market. **Q: Does de-identifying a client communication make it licensable?** Not on its own. Professional conduct rules on confidentiality are broader than privilege, and many jurisdictions require consent or complete removal rather than de-identification. Most legal sellers do not license client communications at all and focus on firm-authored material. **Q: Who reviews the scope on the legal side?** Your own counsel or ethics partner. We help build the manifest and run the process, but we do not give legal advice and we do not decide what a firm is permitted to license. Every legal engagement we take on has the firm's counsel sign off on scope before buyers see anything. ## Industry: Accounting & finance URL: https://datatruffle.ai/industries/accounting-and-finance Accounting firms and finance teams run the same disciplined cycle every month and every year: gather, reconcile, review, adjust, close, report. Buyers building finance and bookkeeping agents want to see that cycle performed by professionals, including the questions asked when the numbers do not tie. What sells: - Month-end close checklists with completed instances, review notes, and sign-offs - Reconciliation workpapers that show the discrepancy, the investigation, and the adjustment - Client request threads: what the accountant asked for, what came back, what was missing - Review and approval trails on financial statements and returns - Onboarding and engagement workflows for new clients - Internal procedures, memos, and position papers the firm authored Workflows buyers value: - Chasing missing documents and reconciling what arrives - Reviewing a junior's work: comments, corrections, re-review - Explaining a variance to a client in plain language - Handling an exception: a late filing, a mismatched balance, a disputed invoice Sensitivities: - Client financial data is confidential under engagement letters and, for tax preparers in the US, under IRC section 7216, which restricts use and disclosure of tax return information without written consent. Client identity and figures are removed or generalized; the procedure is what remains. - Personal data such as Social Security numbers, bank details, and payroll records appears throughout and is removed outright. - Audit workpapers for public companies are subject to retention and confidentiality rules that generally take them off the table. - Internal firm records, meaning your procedures, templates, training, and your own close process, are the clearest path to licensing. Accounting and finance records tend to land in the upper-middle of the range: the workflows are structured and repeated, buyer demand for finance agents is high, and the confidentiality screen is manageable because the method matters more than the client's numbers. **Q: Can we license client ledgers?** Rarely, and usually not worth trying. What buyers want is the process: the checklist, the reconciliation steps, the review comments, the request-and-response with the client. Those can be licensed with client identity and figures removed or generalized, and the value holds up. **Q: How does section 7216 affect a tax practice?** It restricts using or disclosing tax return information for anything other than preparing the return, unless the client gives written consent in the prescribed form. Most tax practices keep return information out of the manifest entirely and license their procedures, templates, and non-return correspondence. Your counsel confirms the line. **Q: Our close process lives in spreadsheets and email. Does that count as a system?** Yes. A spreadsheet checklist with years of completed copies, next to the email threads that drove each close, is exactly the pairing buyers value. The manifest describes where the records live and how they connect; it does not require a particular software stack. ## Industry: Healthcare administration URL: https://datatruffle.ai/industries/healthcare-administration Practices, billing companies, and healthcare administrators run some of the most procedural workflows in any industry: eligibility checks, prior authorizations, claim submissions, denials, appeals, scheduling. Buyers want those administrative sequences. Clinical records are out of scope, and that line is the first thing we draw. What sells: - Prior-authorization workflows: the request, the payer's questions, the outcome - Claims and denial-management sequences, including appeal letters and results - Scheduling and intake procedures with completed examples - Front-desk and billing-office call transcripts, with health details removed - Internal SOPs for coding review, eligibility checks, and patient communication - Payer and vendor correspondence about contracts and reimbursement Workflows buyers value: - Getting a procedure authorized: what the payer asked, how the office answered, how long it took - Working a denied claim from rejection to payment - Handling a scheduling conflict or a no-show policy exception - Explaining a bill to a patient without disclosing clinical detail Sensitivities: - HIPAA. Protected health information is not licensed for AI training under a standard arrangement. Licensable records are limited to administrative workflows with PHI removed to the HIPAA de-identification standard (Safe Harbor or expert determination), and clinical content is excluded as a class. - Business associate agreements. Billing companies and administrators hold data on behalf of covered entities and typically cannot license it without the covered entity's involvement. - Payer contracts often contain confidentiality terms covering reimbursement rates, which are generalized or excluded. - Call recordings need consent review, and any call that drifts into clinical detail is excluded. Healthcare administration tends to land in the middle to upper-middle of the range: the administrative workflows are highly structured and in demand, but the HIPAA screen reduces volume, and billing companies working under business associate agreements may need the covered entity's consent. **Q: Is any of this allowed under HIPAA?** Administrative records with protected health information removed to the HIPAA de-identification standard are no longer PHI, and clinical records are excluded from scope entirely. Whether a particular organization can license its administrative records depends on its role (covered entity or business associate) and its agreements. Your privacy officer and counsel decide; we do not give compliance assurances. **Q: We are a billing company. Whose data is it?** Usually the covered entity's, held under a business associate agreement that limits what you can do with it. Some billing companies license their own operational records, such as SOPs, training, and internal QA, and some secure consent from clients to include de-identified workflow records. The manifest reflects which path you take. **Q: Do prior-authorization records contain PHI?** The originals do. The licensable version keeps the sequence (request submitted, payer asked for X, office responded, decision on day N) and removes patient identity, diagnosis, and procedure detail to the de-identification standard. Buyers want the workflow and the timing, not the patient. ## Industry: Technology & software URL: https://datatruffle.ai/industries/technology-and-software Software companies generate the records buyers want most for coding and support agents: issues, pull requests, reviews, incidents, support tickets, and the documentation that connects them. The records are already structured, timestamped, and linked across systems. The question is usually rights, not relevance. What sells: - Issue-to-merge trajectories: report, triage, implementation, review, release - Support ticket histories that link to bugs and the fixes that closed them - Incident timelines and postmortems - Product documentation with its edit history and the tickets that prompted changes - Design docs and RFC discussions - Sales engineering and customer onboarding threads Workflows buyers value: - Diagnosing a customer-reported bug across support, engineering, and release - Code review as expert feedback on a real change - Responding to an incident and writing up what happened - Deciding what to build: the RFC, the comments, the decision Sensitivities: - Source-code rights. Code you own outright is licensable. Client-owned code, code under copyleft or restrictive licenses, and contractor code without an IP assignment are excluded. - Secrets and credentials appear in logs, tickets, and old commits and are scanned for and removed before any sample. - Customer data in support tickets and logs is personal data and is de-identified; customer contracts may also restrict disclosure of their usage. - Open-source contributions you made to projects you do not own are not yours to license. Technology and software companies tend to land toward the upper part of the range: buyer demand for engineering and support records is the strongest in the market, the data is already structured, and rights are usually clear for proprietary code. **Q: Do we have to include source code?** No. Many software sellers license the records around the code (issues, reviews, incidents, support, docs) and keep the code out, or include only the diffs under review. Where code is included, the license defines the field of use and you choose the repositories. **Q: Our product is built on open source. Does that block a deal?** Using open-source dependencies does not. The manifest covers your own repositories and records with third-party code excluded. Forks of open-source projects and contributions you made upstream are not yours to license, and your counsel confirms ownership of each repository in scope. **Q: Is a coding-agent buyer going to compete with us?** That is what field-of-use terms are for. A license can restrict the buyer to model training and exclude building a competing product from your records. Competing offers make those terms negotiable, which is one reason we run a process rather than take the first offer. ## Industry: Recruiting & staffing URL: https://datatruffle.ai/industries/recruiting-and-staffing A staffing firm runs a two-sided process thousands of times: understand a role, find candidates, screen them, present them, negotiate, place, follow up. Buyers want that sequence with the judgment in it, such as why a candidate was advanced or passed, and how a client's requirements shifted during the search. What sells: - Job intake records: the client brief, the clarifying questions, the agreed spec - Screening notes and interview summaries, de-identified - Candidate-to-placement timelines with the decisions at each stage - Client communication about shortlists, feedback, and offers - Recruiter call transcripts for intake and screening, where consent allows - Playbooks for sourcing, outreach, and negotiation Workflows buyers value: - Turning a vague client ask into a hireable spec - Screening: what was asked, what was heard, why the recruiter advanced or passed - Managing an offer: counter, negotiation, acceptance or decline - Handling a fall-off or a replacement Sensitivities: - Candidate personal data is extensive: resumes, contact details, work history, sometimes compensation and immigration status. All of it is de-identified, and sensitive categories are removed outright. - Anti-discrimination law. Screening notes that reference protected characteristics are excluded, and buyers will ask how that was done. - Data sourced from LinkedIn or job boards is subject to their terms. Your own notes about a candidate are yours; their profile is not. - Client agreements often prohibit disclosing fee structures and the existence of a search. Recruiting and staffing firms tend to land in the middle of the range: the workflows are repeatable and in demand, but the volume of personal data and third-party platform terms narrow what can be licensed cleanly. **Q: Candidates never agreed to this. Can we include their records?** Candidate identity, contact details, and sensitive categories are removed before any sample, so what remains is a de-identified workflow record rather than a record about a person. Whether that is permissible depends on your privacy notices and the law where candidates are. Your counsel reviews that before the manifest is built, and some firms limit scope to intake, client communication, and playbooks. **Q: What about data we pulled from LinkedIn?** Profile data copied from LinkedIn or a job board is subject to that platform's terms and is excluded. Notes your recruiters wrote, call summaries, and the sequence of actions in your ATS are your records. **Q: Our clients would not want it known that they used a recruiter.** Client identity is replaced with consistent fictional values, and the manifest can exclude any client under a confidentiality clause. What a buyer sees is that Client 12 asked for a role, revised the spec twice, and hired the third shortlist. Not who Client 12 is. ## Industry: BPO & call centers URL: https://datatruffle.ai/industries/bpo-and-call-centers Outsourcers and call centers produce the highest-volume conversational records in business: calls, chats, and tickets, each handled under a script, scored by QA, and tagged with an outcome. Buyers building conversation and agent models want exactly that. The complication is that much of it is handled on behalf of clients, which puts the client's rights in the middle of the deal. What sells: - Outcome-tagged call transcripts across campaigns and verticals - QA scorecards with the call they scored and the coaching note that followed - Scripts, call flows, and the recorded deviations from them - Chat and email support histories with resolution codes - Training materials and nesting-period records - Escalation and supervisor-intervention logs Workflows buyers value: - Handling a call from greeting to disposition, with the script and the departures from it - QA review: what was scored, why, and what changed afterward - Escalation: when an agent hands off and what the supervisor did - Onboarding an agent to a new campaign Sensitivities: - Client ownership. Most BPO contracts state that interaction data belongs to the client. Licensing requires either the client's consent or limiting scope to your own operational records: QA frameworks, training, internal process. - Call-recording consent varies by jurisdiction. Two-party-consent states and countries require a clear notice, and that notice must have been given at the time of the call. - PCI DSS. Payment segments are paused or redacted in compliant centers; any that are not are excluded. - Health, financial, and government campaigns carry their own regulatory screens, such as HIPAA, GLBA, and state privacy laws. BPO and call-center records tend to land toward the upper part of the range on volume and demand, but where they land in practice depends on client consent. Centers that own their interaction data, or can secure client sign-off, see the most value. **Q: Our contracts say the client owns the interaction data. Is there anything to license?** Two paths. Your own operational records, such as QA frameworks, scorecards, training material, scripts, and internal process, are typically yours. For interaction data, some clients will consent to de-identified licensing, sometimes with a share of proceeds, because the records are valuable and the risk is managed. The manifest separates the two. **Q: We handle calls for a healthcare client. Can those be included?** Only with the covered entity's involvement and with protected health information removed to the HIPAA standard, and in practice most centers leave healthcare campaigns out. Retail, telecom, utilities, and general customer-service campaigns are more straightforward. **Q: How do buyers treat multilingual or offshore centers?** Favorably, if the consent and rights picture is clean. Buyers want conversational data in many languages and accents, and a center operating across regions can document which jurisdiction's rules applied to each campaign. That documentation is part of the manifest. ## Industry: Logistics & 3PL URL: https://datatruffle.ai/industries/logistics-and-3pl Freight brokers, carriers, and warehouses run on exceptions: a late pickup, a damaged pallet, a reefer out of range, a driver who cannot make the appointment. The records of how dispatchers and account managers handle those moments, hour by hour, are what buyers building operations agents want to learn from. What sells: - Load lifecycle records: quote, book, dispatch, track, deliver, invoice, with the exceptions in between - Dispatch chat and email threads about reroutes, delays, and claims - Carrier and shipper call transcripts with outcomes - Claims and over-short-damaged handling from report to settlement - Receiving, picking, and shipping SOPs with completed checklists - Rate negotiation threads and the booked rate Workflows buyers value: - Covering a load: the search, the calls, the rate, the confirmation - Handling a service failure: notification, options, decision, customer communication - Scheduling appointments around dock and driver constraints - Resolving a claim Sensitivities: - Shipper contracts often treat rates, volumes, and lanes as confidential. Those fields are generalized or bracketed. - Driver personal data such as licenses, hours of service, and locations is removed. Telematics is reduced to events, not tracks. - Carrier and shipper identities are replaced with consistent fictional names so a relationship still reads as one relationship. - Customs, hazmat, and regulated-cargo records may carry additional restrictions. Logistics and 3PL records tend to land in the lower-middle of the range: the exception-handling workflows are genuinely valuable and under-represented in training data, but buyer demand is narrower than for software or finance, and rate confidentiality trims the commercial fields. **Q: Our TMS is old and the data is messy. Does that matter?** Less than continuity does. Eight years on one TMS, even an old one, keeps load numbers, statuses, and notes coherent across the whole history. Buyers expect operational data to be messy; what they price is whether a load can be followed from quote to invoice. **Q: Are rates confidential?** Often, under shipper contracts. Rates can be bracketed, scaled, or excluded while keeping the negotiation sequence, which is what buyers want. The manifest records the choice and your counsel confirms it against your contracts. **Q: Do we need driver consent?** Driver personal data is removed, including names, license details, and location tracks, so what remains is an operational record rather than a record about a driver. Whether that needs notice or consent depends on where drivers are and what your policies say, which your counsel confirms. ## Industry: Real estate & property URL: https://datatruffle.ai/industries/real-estate-and-property Brokerages and property managers coordinate many parties through long, document-heavy processes: listing, showing, offer, inspection, closing; application, lease, move-in, maintenance, renewal. The communication and checklists that move each one forward are what buyers want, with the people and addresses removed. What sells: - Transaction coordination checklists with completed instances and the emails that drove them - Leasing workflows: inquiry, application, screening decision, lease, move-in - Maintenance request histories from report to vendor dispatch to close-out - Owner and tenant correspondence about rent, repairs, and renewals - Showing and follow-up call transcripts, where consent allows - Vendor management and bid comparison records Workflows buyers value: - Getting a deal from accepted offer to closing with every contingency handled - Triaging a maintenance request: urgency, vendor choice, owner approval, follow-up - Negotiating a renewal or handling a late payment - Screening an application against written criteria Sensitivities: - Fair housing. Screening decisions and tenant communications that reference protected classes are excluded, and buyers will ask how screening records were handled. - Addresses are identifying on their own and are replaced with generalized location descriptors. - Financial data in applications, such as income, credit, and bank statements, is removed outright. - MLS data is subject to MLS terms and is not yours to license. Your own correspondence and process records are. Real estate and property records tend to land in the lower-middle of the range: the workflows are rich and repetitive, but the share of identifying information is high, fair-housing screening removes some records, and buyer demand is more selective than in other sectors. **Q: Can a property address really be removed? The whole record is about a property.** Addresses are replaced with a generalized descriptor, such as a two-bedroom unit in a mid-size US metro, and unit identifiers are mapped to consistent fictional values. What buyers want is the sequence (request, triage, vendor, approval, close-out), and that survives without the street address. **Q: What about tenant screening records?** Screening decisions are sensitive under fair-housing law, and application financials are removed outright. Many property managers exclude screening decisions from scope and license the leasing workflow around them: inquiry, application received, lease signed, move-in. Your counsel sets the line. **Q: We are a brokerage with independent agents. Whose records are they?** It depends on your independent contractor agreements and who owns the CRM and transaction platform. Brokerage-owned systems and transaction coordination records are usually the brokerage's; an agent's personal contacts may not be. The manifest reflects what the agreements say. ## Industry: Agencies & marketing URL: https://datatruffle.ai/industries/agencies-and-marketing Agencies turn a brief into finished work through rounds of review, and the rounds are the valuable part. The brief, the first draft, the client's comments, the revision, the approval, and the campaign report that followed show buyers how creative and marketing work is actually judged and improved. What sells: - Brief-to-approval trails: brief, drafts, review comments, revisions, final sign-off - Client feedback threads and how the team responded - Campaign plans with the results reports and the retrospective - Pitch decks and proposals with the outcome, won or lost, and why - Creative review comments on documents and design files - Internal SOPs for onboarding, reporting, and QA Workflows buyers value: - Interpreting an ambiguous brief and getting alignment before work starts - Managing a round of feedback: what the client said, what changed, what was pushed back on - Reporting results and recommending the next move - Scoping and pricing a retainer or a project Sensitivities: - Client ownership of deliverables. Most agency contracts assign final work to the client. The process records (briefs, comments, revisions) are often the agency's, but each master services agreement needs checking. - Unreleased campaigns and embargoed work are excluded until public, and sometimes permanently. - Third-party assets such as stock imagery, fonts, and licensed music are not yours to license and are removed from any sample. - Client performance data, such as spend and results, is usually confidential and is generalized. Agencies tend to land in the middle to lower-middle of the range: review and revision records are distinctive and increasingly requested, but deliverable-ownership clauses and client confidentiality reduce the licensable share. **Q: The final creative belongs to our clients. What do we own?** Usually the process: the brief as you interpreted it, your internal drafts and review comments, the revision history, the project plan, the pitch, and your own SOPs. That is what buyers want anyway, because it shows how work is judged and improved. The manifest keeps client-owned deliverables out. **Q: Can we include client feedback?** Often, with the client de-identified, because the feedback-and-response loop is the most valuable record an agency has. Some master services agreements treat all client communication as confidential, in which case it is excluded. Your counsel checks each agreement in scope. **Q: Our work includes stock photos and licensed fonts. Is that a problem?** Those assets are excluded from any sample because you do not hold the rights to license them onward. The documents, comments, and plans around them are unaffected. A design file with the stock image stripped still shows the review history. ## Guide: Data licensing 101: how a company licenses its records to AI buyers URL: https://datatruffle.ai/guides/data-licensing-101 What a data license is and is not, who buys, what they pay for, how a deal moves from manifest to signature, and what you keep when it closes. ### What a data license is, and what it is not A data license is a contract that lets a buyer use a defined copy of your records, for a defined purpose, for a defined period. It is not a sale. You keep your records, you keep ownership, and you keep the right to license them again unless you agree to exclusivity. The buyer gets a copy and a set of permissions. The closest analogy for most owners is licensing a photograph or a piece of software. The photographer still owns the image; the magazine gets to print it once. In a data license, the AI lab gets to train on a de-identified copy of, say, six years of support tickets. When the term ends, the supplied copy is deleted. The model that was trained still exists, which is why the terms of use matter so much. What it is not: it is not selling your company, not giving anyone access to your live systems, and not handing over customer information. Every serious buyer contracts to de-identify records before training, and a properly structured deal moves a prepared copy once, directly from you to the buyer, after signing. ### Who buys, and why they pay Four kinds of buyer are active right now. Frontier labs building general-purpose models want records that show how real organizations work, because the public web shows what companies say rather than what they do. Data labs assemble and curate training sets on behalf of labs, and often pay faster because they have a standing brief. Model builders working on a specific product, such as a support agent or a coding assistant, want records from the exact workflow their product performs. Enterprise AI teams at large companies buy to fine-tune internal models and tend to want narrow, high-quality sets. They pay because the records are scarce. A model can read every public help center on the internet and still never see a ticket where an agent diagnosed a problem over three replies and closed it with a fix. That sequence, with its outcome, only exists inside companies that have been doing the work for years. Published buyer programs now quote ranges that run from the low six figures into the millions for a single company's operational records, and no serious buyer quotes a number before reviewing a manifest. ### What gets licensed The records that carry value share three properties: they show a process in motion, they end in an outcome, and they exist in volume. In practice that means support tickets , call transcripts , team chat , email threads , CRM histories , engineering records , procedures , and operational systems . What does not get licensed: anything you do not have the rights to, anything privileged, anything regulated in a way that blocks it, and anything your counsel excludes. Personal information is removed or replaced before training, not licensed. The data rights checklist walks through the first three; the de-identification guide covers the last. ### How a deal moves from manifest to signature A deal has six stages, and you approve each one. Qualify. A few questions about headcount, years of records, and systems. Current buyer thresholds are 20 or more desk staff, 3 or more years of records, and data in 3 or more systems. Manifest. A one-page inventory: which systems, which record types, how many years, roughly how much. No records leave your company. This is the document buyers price from. Price. The manifest is benchmarked against what vetted buyers are currently paying for comparable sets. You see the indicative range before any buyer sees your name. First offer. An indicative, buyer-benchmarked range comes back within 24 to 48 hours of a complete manifest. It is a range, not a quote; binding terms follow sample review. Compete. Interested buyers review a small de-identified sample you approve, run diligence, and put terms on the table. Competing offers are the only reliable way to find out what records are worth. Close. You and your counsel pick the terms. You sign directly with the buyer. The prepared copy moves from your systems to theirs. You are paid under the contract. Elapsed time from manifest to signature is typically a few weeks, driven by buyer diligence and your own legal review. The first offer is fast because the pricing work is done before the manifest arrives. ### The six terms that matter most Price gets the attention, but six other terms decide what a license is really worth. Scope. Which systems, which record types, which date range. Everything else is excluded by default. Field of use. What the buyer may do with the copy. Model training is the usual grant. Building a product that competes with you, or reselling the records, should be excluded explicitly. Exclusivity. Non-exclusive by default. If a buyer wants exclusivity, it should be priced separately and limited in time and field. Term and deletion. How long the buyer may hold the supplied copy and when it must be deleted, with written confirmation. De-identification. The standard, who performs it, who verifies it, and what happens if the standard is missed. Payment. How much, when, and in what installments. Net 30 to net 60 from signing is common in published buyer programs. The value guide explains how each term moves the number. ### What you keep You keep your records, your systems, and your ownership. You keep the right to license to other buyers unless you grant exclusivity. You keep the right to walk away at any point before signing. You keep control of scope: a buyer who wants more than the manifest offers has to negotiate for it. What you give up is a copy, for a period, for a purpose. A model trained on that copy keeps what it learned after the copy is deleted, so the field-of-use and competition terms are the ones to read twice. ### Where to start You do not need to export anything, build a dataset, or hire anyone to find out whether your records are worth licensing. The starting point is a manifest, and the starting point for a manifest is seven questions. If your company qualifies, the manifest takes a day and the first indicative range follows within 24 to 48 hours. Before any of that, run the red-flags checklist against anyone who approaches you, including us. A real opportunity should survive it. Key takeaways: - A license is a copy plus permissions, for a period, for a purpose. You keep ownership and, by default, the right to license again. - Buyers pay because process-in-motion records with outcomes do not exist on the public web. - A manifest, not a dataset, is what buyers price from. No records leave your company until you sign. - Scope, field of use, exclusivity, term, de-identification, and payment timing decide what a license is really worth. **Q: Do we lose ownership of our data?** No. A license grants a buyer defined rights to use a copy; ownership of the underlying records stays with you. Unless you agree to exclusivity, you can license the same records to another buyer. **Q: How long does the whole process take?** The first indicative range arrives within 24 to 48 hours of a complete manifest. Sample review, diligence, and negotiation typically take a few weeks after that, depending on buyer timelines and your own counsel's review. Payment terms are set in the contract. **Q: Is this legal?** Licensing records you own, with personal information removed and contractual restrictions respected, is a conventional commercial arrangement. Whether a specific set of records can be licensed depends on your contracts, your industry's rules, and your counsel's review. We do not provide legal advice and every deal we run has the seller's counsel involved before signing. ## Guide: The data rights checklist: what to confirm before you license anything URL: https://datatruffle.ai/guides/data-rights-checklist Access is not permission. The ownership, contract, personal-data, regulatory, and platform questions to settle before records go in a manifest. ### Access is not permission The fact that records sit in your systems does not mean you can license them. Some belong to clients. Some are covered by contracts you signed years ago. Some contain information about people who never agreed to anything. Some are regulated. A good manifest sorts records into what you can license cleanly, what you can license with conditions, and what stays out. This checklist is the sorting process. It is not legal advice, and the last step on every item is your own counsel. What it does is make sure the right questions get asked before a buyer sees anything, so the deal does not fall apart in diligence. ### Ownership: is it yours? Start with who created the record and under what arrangement. Employee work product is normally the company's. Check that employment agreements say so, especially for staff in jurisdictions where the default runs the other way. Contractor work product is the contractor's unless an agreement assigns it. Code, designs, and documents written by contractors without an IP assignment are excluded until that is fixed. Client-owned deliverables. Many service contracts assign the final deliverable to the client. Your process records around it (proposals, drafts, reviews, internal notes) are usually still yours. Third-party content such as vendor manuals, licensed templates, stock assets, and anything copied from a platform is not yours to license onward. Acquired companies. Records from an acquisition are yours if the purchase agreement transferred them. Check the schedule. ### Contracts: what did you promise? Pull the agreements that touch the records in scope and look for four kinds of clause. Confidentiality. Most customer and client agreements restrict disclosure of the other party's information. De-identification often satisfies this, but some clauses are drafted broadly enough to require consent or exclusion. Standalone NDAs usually mean exclusion. Data ownership and use. Outsourcing, BPO, and managed-service contracts frequently state that interaction data belongs to the client. If so, licensing needs the client's consent or scope limited to your own operational records. Vendor and platform terms. Your CRM, helpdesk, and chat providers have terms that govern export and reuse. Most permit export of your own data; a few restrict commercial redistribution. Read the current version. Exclusivity you already granted. If you have licensed records before, check whether that license was exclusive and for what field. Where a clause is unclear, the conservative answer is to exclude the affected records from the manifest and revisit once counsel has read the agreement. Excluded records can be added later; a breach cannot be undone. ### People: whose information is in it? Operational records are full of personal information: customer names, employee names, contact details, sometimes much more. Licensing does not require that information. De-identification removes or replaces it before any sample is built. The rights question is whether your privacy notices and the law where people live permit the use. Customers and contacts. Check your privacy notice and the law in your customers' jurisdictions. De-identified data is treated differently from personal data in most frameworks, and the de-identification standard in the contract should be one your counsel accepts. Employees. Their messages and work product are in nearly every record type. Employee privacy notices and, in some countries, works-council or union consultation apply. Direct messages and HR matters are excluded by default. Callers. Recorded calls need a consent record. Two-party-consent jurisdictions require that the notice was given at the time. Audio is more sensitive than transcripts. Candidates, tenants, patients, and other individuals with their own regulatory protections are covered under the next heading. ### Regulated categories Some records carry rules that go beyond ordinary privacy law. The usual approach is to exclude the regulated content as a class and license the workflow around it. Category What applies Usual treatment Health information HIPAA in the US, equivalents elsewhere; business associate agreements Clinical content excluded; administrative workflows de-identified to the HIPAA standard Legal matters Attorney-client privilege, work product, professional conduct rules Privileged classes excluded entirely; firm-authored precedents and process records licensed Payment data PCI DSS Card and bank numbers removed outright Tax return information IRC section 7216 for US preparers Return information excluded; procedures and non-return correspondence licensed Candidate and tenant screening Anti-discrimination and fair-housing law Screening decisions excluded or heavily restricted; workflow records around them licensed Children's data COPPA and equivalents Excluded Financial services customer data GLBA and equivalents Excluded or de-identified with counsel review This table is a starting point, not a complete list, and the rules change. Your counsel decides what applies to you. ### Platform terms Two platform questions come up in nearly every deal. First, does your software vendor's agreement allow you to export and license your own data? For the mainstream business systems the answer is generally yes, with the caveat that data the vendor supplied, such as enrichment fields or benchmark data, is theirs. Second, did any records originate on a third-party platform whose terms restrict reuse? Profile data copied from a professional network, listings pulled from a multiple listing service, and content scraped from the web are the common cases, and they are excluded. Your own notes about those records are yours. ### Writing it into the manifest The manifest is where the checklist lands. For each system and record type it records three things: the rights basis (owned outright, owned with conditions, or excluded), the exclusions applied (by class, by counterparty, by date), and the de-identification rules. Buyers read this section first, because it tells them whether a deal can close. A manifest with clear rights and sensible exclusions attracts better offers than a larger one with open questions. If your counsel has not seen a data license before, the licensing 101 guide and the glossary are a useful briefing. If you want to know how rights clarity affects price, read what drives value . Key takeaways: - Sort records into owned outright, owned with conditions, and excluded before a buyer sees anything. - Check employment, contractor, client, outsourcing, and platform agreements for ownership and confidentiality clauses. - Personal information is removed or replaced rather than licensed; the rights question is whether your notices and local law permit the use. - Regulated categories (health, legal, payment, tax, screening) are usually excluded as a class with the workflow around them licensed. **Q: Does de-identification solve the confidentiality problem?** Often, but not always. Many confidentiality clauses are satisfied once the counterparty cannot be identified. Some are drafted broadly enough that consent or exclusion is the safe route. Your counsel reads the clause; the manifest records the decision. **Q: What if we find a problem after the deal closes?** A well-drafted license has a procedure for it: notice to the buyer, removal of the affected records from the supplied copy, and in serious cases a cure period or termination. It is far cheaper to exclude a doubtful class before the manifest goes out than to invoke that procedure later. **Q: Do you check our rights for us?** We help you build the manifest and we ask the questions on this checklist, but we do not give legal advice and we do not decide what you are permitted to license. Your counsel does. Every seller we represent has counsel review the manifest before buyers see it. ## Guide: The red-flags checklist: run it against anyone, including us URL: https://datatruffle.ai/guides/red-flags-checklist Nine warning signs in a data-licensing approach, why each one matters, how we score against our own list, and what to do if you see one. ### Why a checklist, and why we include ourselves This market is less than two years old. Most of the companies in it have no public track record, including us. When nobody can point to a logo wall, the honest alternative is to publish the questions a careful owner should ask and answer them in writing. Below are nine red flags we see in the market, why each one matters, and our own answer to each. If we fail any of them, walk away. ### The nine red flags An upfront fee. Listing fees, preparation fees, retainers, valuation fees. A legitimate seller-side intermediary is paid from a closed deal. If someone needs money before a buyer has made an offer, their business model is selling to you, not for you. A request for raw data before a signed agreement. Buyers price from a manifest and, later, a small de-identified sample you approve. Nobody needs your database to tell you what it is worth. Anyone who asks for a system login, an export, or a drive link on the first call is either careless or collecting data for their own purposes. A refusal to name the buyer. You are going to sign a contract with the buyer and send them a copy of your records. You need to know who they are, what they do, and what they will contract to. An intermediary who will not say who ends up with the data before you commit is hiding either the buyer or the fee. A dollar promise before review. Ranges are fine when they are labelled as ranges and sourced to what buyers actually pay. A specific number before anyone has seen a manifest is a sales tactic. Published buyer programs themselves say they price after review. Pressure to decide on the first call. There is no reason a licensing decision has to be made in a conversation. A real opportunity will still be there after you have talked to your counsel. Exploding offers. Offers that expire in 48 hours or two weeks exist to stop you getting a second number. A buyer who wants your records this week will want them next week too. If an offer has a fuse, ask why. Exclusive broker lock-ins. Some intermediaries require a months-long exclusive agreement before they will introduce you to anyone, sometimes tied to a single buyer. That removes your ability to compare and removes their incentive to perform. A seller-side engagement should be non-exclusive and cancellable. Undisclosed referral fees. Many introduction services are paid by the buyer when a deal closes. That is not wrong in itself, but it has to be disclosed, because it means the intermediary's customer is the buyer. If you cannot find a clear statement of who pays whom, assume the answer is not in your favor. Logos implying affiliation. A row of AI lab logos with no deal behind them, press logos from wire syndication, and claims of being approved or certified by labs that do not run such programs. Ask which of the logos represent a signed agreement. ### Three questions that surface most of them You do not need to memorize the list. Three questions on a first call surface most of the flags at once. Who pays you, how much, and when? A clear answer names the party, the basis, and the timing. Hedging on any of the three usually means a buyer-side fee or a hidden one. Who will I be signing with, and can I see their standard terms before I commit to anything? An intermediary working for you will name the buyer and hand over the paper. One working for the buyer will stall. What do you need from me before I see a number? The right answer is a description of your records. Any answer that involves a login, an export, a fee, or a signature is a flag. Write the answers down. If they change later in the process, that is a flag of its own. ### How we score against our own list Flag Our answer How to verify Upfront fee None. Success fee only, paid from proceeds when a license closes. The fee schedule is public and the engagement letter says the same. Raw data before signing Never. We price from a manifest and never receive datasets at all. Read the custody page ; ask us for a system login and watch us decline. Naming the buyer Every buyer is named before you decide anything. Ask on the first call. Buyers are described by category on this site and by name in your process. Dollar promise before review We quote indicative ranges labelled as such, sourced to live buyer demand. No number is a quote until sample review. Every figure on this site carries the indicative-range disclaimer. Pressure on the first call None. Our fit call is 20 minutes and ends with a next step, not a signature. Book one . Exploding offers We do not run them and we tell buyers we will not relay them. Ask us to put it in the engagement letter. Exclusive lock-in The engagement letter is non-exclusive and cancellable. Read it before the first call; it is two pages. Undisclosed referral fees We take no buyer-side fees. If that ever changes, it will be on the pricing page first. Pricing page ; ask us in writing. Logos implying affiliation No lab logos, no press logos, no testimonials until we have real ones with permission. Look at this site. We would rather earn trust by being checkable than by being impressive. ### What to do if you see one One flag is a reason to ask a question. Two is a reason to slow down. Three is a reason to stop. In every case, the move is the same: do not share data, do not sign anything, and get a second number from someone who is not the party in front of you. If you already have an offer, we will benchmark it against current buyer demand within 24 hours and tell you whether it is in range, with no obligation. The point is not to steer you to us. The point is that a credible second number is the best price protection an owner has, and it is the one thing every red flag on this list is designed to prevent. Before you talk to anyone, read the data rights checklist so you know what you are permitted to license, and the licensing basics so you know what a fair deal looks like. Key takeaways: - No legitimate seller-side intermediary needs money, data, or a signature before a buyer has made an offer. - A refusal to name the buyer or to state who pays the intermediary means the intermediary is not working for you. - Exploding offers and exclusive lock-ins exist to stop you getting a second number. Get one anyway. - Run this list against us. Every answer above is checkable on this site or in a two-page engagement letter. **Q: Is it a red flag if an intermediary is paid by the buyer?** Not on its own, if it is disclosed. Buyer-paid introduction services are common and some are useful. The red flag is non-disclosure, because a buyer-paid intermediary's incentives follow the buyer. Ask who pays, how much, and whether the introduction binds you to anything. **Q: What if a buyer approaches us directly?** Treat it the same way. A direct buyer is not an intermediary, but exploding offers, requests for raw data before signing, and pressure to decide quickly are the same warning signs. Get the offer in writing and get a second number before you respond. **Q: How can we check your claims if you have no track record?** By reading the documents: the public fee schedule, the two-page non-exclusive engagement letter, the custody page, and the names of the buyers we bring to your process. None of those requires taking our word for it, which is the point. ## Guide: De-identification, explained: what gets removed, what gets replaced, what stays URL: https://datatruffle.ai/guides/de-identification-explained The three operations behind de-identification, a worked example, why consistency is the hard part, what the process cannot do, and what to put in the contract. ### The goal: keep the workflow, lose the person The value in operational records is the sequence: a request came in, someone asked a question, a decision got made, something happened next. None of that depends on knowing who the customer was. De-identification is the process of keeping the sequence and removing the people, the companies, and the details that would point back to them. It is done before training and, in a properly structured deal, it is a contractual obligation of the buyer with a standard, a verifier, and a remedy. You approve the rules, you review a small sample built under those rules, and nothing is licensed until you are satisfied with what you see. ### Three operations: remove, replace, keep Every field in a record gets one of three treatments. Remove. The value is deleted or replaced with a placeholder such as [removed]. Used for anything that is identifying and carries no workflow value: card numbers, bank details, government ID numbers, street addresses, dates of birth, attachments with personal data. Replace. The value is swapped for a consistent fictional one. Jordan Lee becomes Person A everywhere Jordan Lee appears. Harbor Dental becomes Company 01 in every ticket, email, and deal. Used for names, email addresses, phone numbers, account identifiers, and company names, where the structure of the record matters and the identity does not. Keep. The value stays. Used for the content of the work: the problem described, the question asked, the fix applied, the status, the timestamps (often coarsened to a week or quarter), the amounts where they are not identifying. Removal is simpler and safer; replacement preserves more value. A good set of rules uses removal for the dangerous fields and replacement for the structural ones. ### A worked example A fictional support ticket, before and after. Replacements are made up. Field Original De-identified copy Operation Ticket #48213, 2024-09-04 Ticket 001, 2024-Q3 Replace, coarsen Requester Angela Ferreira, aferreira@summitpayroll.example Requester A, requester-a@company-02.example Replace Phone (555) 010-0142 [removed] Remove Subject Direct deposit file rejected by bank Direct deposit file rejected by bank Keep Internal note Routing number for Summit's new account keyed wrong at setup. Fixed, re-sent. Routing number for the customer's new account keyed wrong at setup. Fixed, re-sent. Replace in text Resolution Solved in 2h 11m, CSAT 5/5 Solved in 2h 11m, CSAT 5/5 Keep Notice what survived: the problem, the diagnosis, the fix, the time to resolve, the satisfaction score. That is the whole training value of the ticket. Notice what did not: anything that would let a reader find Angela or Summit Payroll. ### Consistency is the hard part The difference between amateur and professional de-identification is consistency. If Requester A is a different fictional person in every ticket, a buyer loses the fact that the same customer raised five related tickets over two years, which is part of what they are paying for. If Requester A is the same fictional person everywhere, the structure survives. Consistency has to hold across systems as well as within them. The customer in the CRM, the requester in the helpdesk, and the counterparty in the email thread are the same company, and they should map to the same fictional identity in all three. That is why de-identification is done against the whole licensed set with a single mapping table, and why the mapping table itself is treated as the most sensitive artifact in the deal. It stays with the seller or a named verifier and never goes to the buyer. Free text is where consistency breaks. Names appear inside sentences, in signatures, in forwarded messages, in nicknames. Modern tooling handles most of it; the sample review is where you catch the rest. Expect to iterate on the rules once or twice before the sample is clean. ### What de-identification cannot do Three honest limits. It cannot be perfect. No process removes re-identification risk entirely, and anyone who says 100 percent is overselling. The standard to ask for is a documented one, applied by a named party, verified by sampling, with a remedy if it is missed. It cannot undo training. Deleting the supplied copy at the end of the term does not remove what a model learned from it. That is why field-of-use terms matter as much as deletion terms. It cannot fix a rights problem. De-identifying a record you were not permitted to license does not make it licensable. Privileged, regulated, and client-owned classes are excluded before de-identification starts. The rights checklist covers that. ### Who does it, and who checks Three arrangements are common. The buyer de-identifies on receipt under a contractual standard; this is the most frequent model in published buyer programs and works when the standard, verification, and remedy are written down. The seller de-identifies before transfer, which keeps the raw records in-house but requires tooling and effort on your side. A named third party does it under contract to both sides, which is the cleanest chain of custody and costs money that comes out of the deal. Whichever arrangement you choose, the records move directly from you to whoever is doing the work, after signing. We never receive, hold, or process datasets. Our role is to make sure the standard is in the contract and the buyer has agreed to it before we introduce them. ### What to put in the contract Six clauses turn de-identification from a promise into an obligation. The standard, by name, or the specific rules if there is no named standard that fits. Who performs it and when: before training, within a stated number of days of receipt. Who verifies it, how (sampling rate, method), and who sees the verification report. What happens on a miss: notice, remediation, and the right to require deletion of affected records. Custody of the mapping table: who holds it, who may never receive it. Deletion of the supplied copy at the end of the term, with written confirmation. If a buyer will not agree to these, that is useful information. The buyers we work with have already agreed to them before an introduction is made. Key takeaways: - De-identification keeps the workflow (problem, diagnosis, fix, timing) and removes or replaces the people and companies. - Replacement with consistent fictional values preserves structure; removal is for fields that are dangerous and carry no workflow value. - Consistency across systems is the hard part, and the mapping table never goes to the buyer. - No process is perfect, deletion does not undo training, and de-identification cannot fix a rights problem. Put the standard, verifier, and remedy in the contract. **Q: Who does the de-identification?** Most commonly the buyer, on receipt, under a contractual standard with verification. Sellers can do it before transfer if they have the tooling, and a named third party can do it for both sides. In every case, the records go directly from you to whoever does the work, never through us. **Q: Can our customers be re-identified?** The risk cannot be reduced to zero, which is why we do not use the phrase 100 percent de-identified. It can be reduced to a documented, verified, contractually enforced standard, with a remedy if the standard is missed. Your counsel decides whether that standard is acceptable for your records. **Q: Do we see the de-identified copy before it goes out?** You see and approve a sample built under the agreed rules before anything is licensed, and you can require changes to the rules until the sample is clean. The full copy is produced under those same rules. ## Guide: What drives value: the factors that move a data license up or down the range URL: https://datatruffle.ai/guides/what-drives-value There is no price list. Six factors that move an offer, what does not move it much, and how to find out where your records land before anyone names a number. ### There is no price list Owners ask what their data is worth and expect a rate per gigabyte. There is not one. Two companies with the same headcount and the same systems can get offers that differ by a factor of five, because buyers are paying for signal, and signal depends on things a file size does not capture. Published buyer programs quote ranges from the low six figures into the millions for a single company's records, and the spread inside that range is explained almost entirely by the six factors below. Every figure here is indicative. Actual value depends on buyer demand on the day, which is why we benchmark a manifest against live demand rather than against a formula. ### Years of continuous history The single biggest driver. Buyers want to see a process repeated enough times that a model can learn the pattern and its exceptions. Three years is the current threshold; five to ten years in the same systems is where the number climbs. Continuity matters more than length. A migration in year four that dropped the internal notes leaves you with four good years and four thin ones, and buyers price the two periods differently. Our estimator counts up to ten years. Beyond that, buyers have usually seen enough of the pattern, and old records increasingly reflect tools and practices that have changed. ### Outcomes attached A ticket that ends in a documented fix is worth several times a ticket that ends in nothing. A call tagged with its disposition is worth more than an untagged recording. A deal that closed won or lost with a reason is worth more than one left open. Outcomes turn a record into a labelled example, and labelled examples are what buyers are short of. Practically, this is why support queues with high resolution rates, CRMs where reps record lost reasons, and call centers with QA dispositions land toward the top of the range. If your team has been lazy about closing out records, the manifest will show it and the number will too. Starting to close records properly now improves the value of everything you produce from here on. ### Volume and density Volume matters, but density matters more. A 30-person company whose records are almost entirely about work outranks a 100-person company whose chat is mostly social and whose email is mostly notifications. Buyers screen samples for the share of records that carry workflow signal and discount the rest. The record types with the highest demand right now are calls with outcomes and support tickets , followed by engineering records and team chat . Email, CRM, documents, and operational systems carry steady demand. Having several of these in the same company compounds the value, which brings us to linkage. ### Linkage across systems The most valuable thing a company can offer is a single piece of work that can be followed across systems: the email that became a ticket, the ticket that became a bug, the bug that became a release, the release that closed three more tickets. Each record alone is a snapshot. Linked, they are a trajectory, and trajectories are what agentic models are trained on. This is why the qualification threshold asks for records in three or more systems. It is also why the manifest records the identifiers that connect them: ticket numbers in chat, deal IDs in email, customer IDs across CRM and billing. You do not need the systems to be integrated. You need the references to exist. ### Rights clarity A set with clear rights and sensible exclusions closes. A larger set with open questions stalls in diligence and often closes smaller, later, or not at all. Buyers price in the risk, so the same records are worth more when the rights checklist has been worked through before the manifest goes out. Regulated and client-owned classes excluded cleanly cost less value than you would expect, because buyers were going to discount them anyway. ### Exclusivity and field of use The terms you grant change the price more than any property of the data. Non-exclusive is the default and lets you license the same records to a second buyer. Most deals are non-exclusive. Exclusive licenses command a premium, often a large one, because the buyer is paying for everyone else not to have the records. Exclusivity should be limited in time and field, and priced separately so you can see what you are being paid for it. Field of use narrows what the buyer can do. A license limited to model training is worth less to the buyer than one that also permits product development, and the price should reflect which you grant. Refresh rights , where the buyer pays again for new records on a schedule, turn a one-time license into recurring revenue and are worth negotiating for if your records keep accumulating. Competing offers are what make these terms negotiable. One buyer presents its template; three buyers present terms you can play against each other. ### What does not move the number much A few things owners expect to matter that mostly do not. Your industry's prestige: buyers want workflow, and a plumbing dispatcher's exception handling can be worth as much as a consultancy's. Your company's revenue: unrelated to the data. Raw storage size: a terabyte of attachments is worth less than a gigabyte of tickets. Whether you are a technology company: the market is explicitly looking for non-technology operational records. The industry pages on this site describe where each sector tends to land and why, without pretending that sector alone decides it. ### How to find out where you land The estimator gives a rough range from headcount, years, systems, and record types in about a minute, and shows its factors. It is a heuristic calibrated to published buyer ranges, not a quote. A real range takes a manifest, which takes a day, and comes back benchmarked against live buyer demand within 24 to 48 hours. Binding terms follow sample review. If you want to know what your records are worth, that is the shortest honest path to the answer. Key takeaways: - Years of continuous history in the same systems is the biggest single driver, and continuity beats length. - Outcomes attached (fixes, dispositions, won or lost with a reason) turn records into labelled examples and multiply value. - Linkage across systems, where one piece of work can be followed from email to ticket to release, is what agentic buyers pay most for. - Exclusivity and field of use change the price more than any property of the data, and competing offers are what make them negotiable. **Q: Why do you give a range instead of a number?** Because the number depends on which buyers are active, what they are short of that month, and the terms you choose to grant. A range sourced to live demand is honest; a single figure before sample review is not. Binding offers come after buyers review a de-identified sample. **Q: Can we improve our value before going to market?** Yes, cheaply. Close out open tickets and deals with outcomes, stop deleting old records, keep the internal notes when you migrate systems, and work through the rights checklist so the manifest has no open questions. None of that requires new software. **Q: Is bigger always better?** No. Density, outcomes, and linkage matter more than size. A 30-person specialist firm with eight years of resolved tickets and a CRM full of lost reasons will often outrank a much larger company with thin, unlinked records. ## Compare: Going direct to one lab vs. a seller-side agent URL: https://datatruffle.ai/compare/going-direct Most owners who license their data get exactly one offer, from the first lab that emailed them. Going direct is free and sometimes right. Here is what it costs you in price discovery, terms, and negotiating position, and when it is still the better call. - Price discovery: them — You learn what one buyer will pay, which is usually its opening number. There is nothing to compare it with.; Data Truffle — The manifest is benchmarked against live demand from several vetted buyers before anyone sees your name. You know the range before the first conversation. - Number of offers: them — One. If the lab passes, you start over with the next one.; Data Truffle — Qualified sets go to more than one buyer at once. Competing terms are the only reliable way to find out what records are worth. - Time to first number: them — Depends on the lab's queue. Published programs quote anywhere from a day to two weeks for an initial review, and a single lab can be fast.; Data Truffle — An indicative, buyer-benchmarked range within 24 to 48 hours of a complete manifest. Binding terms follow sample review. Fast labs can match the speed for their own number; none match it with a competitive one. - Who pays the intermediary: them — There is no intermediary, so nobody. There is also nobody whose job is to get you the best terms.; Data Truffle — You do, as a success fee from proceeds, only when a license closes. That is why we work for you and take no buyer-side fees. - Whose side: them — The lab's counsel and data team represent the lab. You represent yourself, usually for the first time.; Data Truffle — We represent you and only you. Every buyer is named before you decide, and we take nothing from them. - Custody: them — Direct from you to the lab after signing, if you insist on it. Some lab templates ask for access earlier.; Data Truffle — Direct from you to the buyer after signing, always. We never receive, hold, or process datasets, so there is no second copy to worry about. - De-identification enforcement: them — Whatever the lab's standard contract says. You negotiate the standard, the verifier, and the remedy alone, or you do not.; Data Truffle — Every buyer we introduce has already agreed to a documented de-identification standard, verification, and deletion schedule before the introduction is made. - Exclusivity and field of use: them — Lab templates often default to broad, perpetual, exclusivity-leaning terms. You have to know what to push on and have a competing offer to push with.; Data Truffle — Non-exclusive by default. Exclusivity and field of use are priced separately and negotiated against competing offers, which is the only leverage that works. - Exit cost: them — None before signing. After signing, whatever the license says.; Data Truffle — None before signing. The engagement letter is non-exclusive and cancellable; walk away at any point with nothing owed. - Fee: them — No fee. You keep the whole offer, whatever the offer is.; Data Truffle — A success fee from proceeds, on the schedule published on the pricing page. On a competitive process it is usually smaller than the difference between the first offer and the best one, but that is not guaranteed. When they win: Going direct is the better choice when you already know which buyer you want, you have counsel who has negotiated an AI data license before, and you have a strategic reason to prefer one lab, such as an existing relationship or a product integration. It is also the right call for small sets that would not attract competing bids. If you do go direct, get a second number first. We will benchmark an offer you already have within 24 hours, with no obligation. ## Compare: Listing on a data marketplace vs. a seller-side agent URL: https://datatruffle.ai/compare/marketplaces Marketplaces are the right answer for some datasets. For a single company's operational records, with personal information inside and rights to manage, they usually are not. Here is the comparison, dimension by dimension, and the cases where a marketplace is the better choice. - Price discovery: them — You set a list price, or the marketplace suggests one from its catalogue. Discovery happens after listing, if buyers come.; Data Truffle — The manifest is benchmarked against live demand from vetted buyers before anything is listed anywhere. You see the range first. - Number of offers: them — As many as browse and bite. For a one-off operational set from a single company, that is often none; marketplaces favor reusable corpora.; Data Truffle — Qualified sets are taken to more than one buyer who has a current brief for that kind of record. - Time to first number: them — Listing takes minutes. A first buyer may take weeks or never arrive.; Data Truffle — An indicative range within 24 to 48 hours of a complete manifest, from buyers who are actively buying. - Who pays the intermediary: them — You do, as a commission from proceeds. Published schedules run from a few percent at scale to a large share on smaller sales.; Data Truffle — You do, as a success fee from proceeds, only when a license closes. The schedule is public. - Whose side: them — The platform's. It serves both sides and earns on volume, not on your terms.; Data Truffle — Yours. We represent the seller only and take no buyer-side fees. - Custody: them — Most marketplaces host, scan, or process the data to list it. A copy of your records sits on a platform you do not control.; Data Truffle — Your datasets never pass through us. The only transfer is from you to the buyer you approve, after signing. - De-identification enforcement: them — Your responsibility before upload. Once listed, the platform's standard terms govern what a buyer may do with it.; Data Truffle — Every buyer contracts to a documented standard, a verifier, and a remedy before introduction. You approve a sample built under those rules. - Exclusivity and field of use: them — Standard license terms, take them or leave them. Exclusivity and narrow fields of use are rarely available.; Data Truffle — Negotiated per deal against competing offers. Non-exclusive by default; exclusivity priced separately. - Exit cost: them — Delist any time, but copies already downloaded stay downloaded, and some platforms keep the scan.; Data Truffle — Walk away before signing with nothing owed. Nothing has left your systems. - Fee: them — A commission that can be lower than a success fee at high volume or for repeat sales of the same set.; Data Truffle — A success fee from proceeds on a published schedule. For a one-time license of a single company's records, comparable; for recurring sales of a reusable product, often higher. When they win: A marketplace is the better choice when you have a clean, well-documented, reusable dataset that many buyers want in the same form, with no personal information and clear rights, and you want recurring sales rather than one license. Public datasets, labelled corpora, and productized data fit well. A single company's operational records, with people inside and contracts to respect, rarely do, and listing them publicly can create the rights problem a private process avoids. ## Compare: Buyer-paid introduction routers vs. a seller-side agent URL: https://datatruffle.ai/compare/introduction-routers A large share of the sites offering to connect business owners with AI labs are referral services: they pass your details to a buyer's partner program and collect a fee from the buyer when a deal closes. Some are useful. None of them are working for you. Here is the comparison and the cases where a router is still the right call. - Price discovery: them — None. You are routed to the buyer whose referral program pays the router, and that buyer names its number.; Data Truffle — The manifest is benchmarked against live demand from several vetted buyers before anyone is introduced. You see the range before the buyer sees your name. - Number of offers: them — One, from the buyer you were routed to. A few routers offer a choice of programs, but you still negotiate each one alone.; Data Truffle — Qualified sets go to more than one buyer at once, and competing terms are played against each other. - Time to first number: them — Introductions are fast, often within a day. The number then arrives on the buyer's own timeline.; Data Truffle — An indicative range within 24 to 48 hours of a complete manifest, then competing terms after sample review. - Who pays the intermediary: them — The buyer, as a referral or origination fee when you sign. It is frequently undisclosed, and sometimes substantial.; Data Truffle — You do, as a success fee from proceeds when a license closes. We take nothing from buyers, which is the only way to be on one side. - Whose side: them — The buyer's. The buyer is the router's customer; you are the product being referred.; Data Truffle — Yours. Every buyer is named before you decide, and the engagement letter says who we work for. - Custody: them — Direct from you to the buyer. The router never sees the data, which is one thing they get right.; Data Truffle — Direct from you to the buyer after signing. We never receive datasets either. - De-identification enforcement: them — Whatever the buyer's standard contract says. The router has no role after the introduction and no stake in the terms.; Data Truffle — Every buyer has agreed to a documented standard, verification, and deletion schedule before we introduce them, and we stay through signing. - Exclusivity and field of use: them — The buyer's template. Nobody on your side is reading it, and there is no competing offer to negotiate against.; Data Truffle — Non-exclusive by default. Exclusivity and field of use priced separately and negotiated against competing offers. - Exit cost: them — Usually none. Some routers require an exclusive broker agreement for months, tied to one buyer, before they will introduce you; read the terms.; Data Truffle — None before signing. Non-exclusive, cancellable engagement letter. - Fee: them — Nothing out of your pocket. The buyer's referral fee is priced into what the buyer is willing to pay you, but you never see the line item.; Data Truffle — A success fee from proceeds on a published schedule. Visible, and paid only when you are paid. When they win: An introduction router is the better choice when you have already decided which buyer you want, you only need a warm introduction, and you have counsel ready to negotiate the buyer's paper on your behalf. It costs you nothing out of pocket. Before you use one, ask in writing what the buyer pays them, whether the introduction binds you to an exclusive period, and whether you can still take competing offers. If the answers are clear and acceptable, a router is a reasonable door to walk through. ## Blog: What drives the value of a dataset URL: https://datatruffle.ai/blog/what-drives-the-value-of-a-dataset Published: 2026-11-05 Five things move the number more than anything else: years of history, volume, outcome labels, modality, and rights. How each one changes what a buyer will pay, and what you can do about it before going to market. Owners ask the same question in the first call: what is this worth? The honest answer is a range, and the range is wide, because the same company can be worth very different amounts to different buyers. But the variables that set the range are not mysterious. Five of them move the number more than everything else combined. Here they are, in roughly the order a buyer reads them on a manifest. [diagram] ## Years of history Depth is the first thing a buyer checks, and the one thing you cannot manufacture. A dataset covering six years of the same workflow shows how a process behaves across staff turnover, product changes, busy seasons, and at least one bad quarter. Two years shows a snapshot. Continuity matters as much as length. Ten years with a helpdesk migration in the middle that dropped the first seven is a three-year dataset. Say so on the manifest; buyers will find it at sample review anyway. The qualification we use starts at three years. Below that, the right advice is usually to wait and keep the records. ## Volume Volume is the number of records, and it scales with headcount and time. Twenty desk staff producing tickets, deals, threads, and documents for five years generate a body of work large enough to learn from. Five people for one year do not. Buyers think about volume per workflow rather than in total. Forty thousand support tickets is a usable set on its own; four thousand tickets plus four thousand emails plus four thousand files is three thin sets. Concentration in a few deep workflows generally prices better than a little of everything. The reported Spirit Airlines data sale is a useful public reference for how volume gets described. The asset was reported as roughly 100 million emails, 500 million chat messages, and 30 million lines of code. Most companies are two or three orders of magnitude smaller, and that is fine. The point is that buyers read volume in those terms, per system, and a manifest should give it to them that way. ## Outcome labels This is the driver that separates two otherwise identical companies. A record with an outcome shows not just that work happened but how it ended. A ticket with a resolution code and a satisfaction score. A deal marked closed-won or closed-lost with a reason. A dispute with a ruling. A code review that was merged or rejected, with the comments attached. Buyers pay for outcomes because models learn from them. A million messages with no resolution teach a model how people talk. Fifty thousand tickets with resolutions teach it how problems get solved. The second is rarer and more valuable. If your systems already capture outcomes, make sure the manifest says so, per system, with a rough share of records that carry them. If they do not, start now. It costs nothing and changes the price in two years. ## Modality Modality is the form the records take. Text is the baseline: tickets, email, chat, documents. Structured records such as CRM stages and billing events add value when they can be joined to the text. Audio, meaning recorded calls, is scarcer and prices higher when consent exists and transcripts are available. Code and engineering workflows are a distinct category with their own buyers. Two things matter here. First, the mix: a company with tickets, the chat where the ticket was discussed, and the call that preceded it has something a single-channel export does not. Second, usability: a native export with attachments is worth more than a lossy one, and audio without transcripts is a project rather than a dataset. ## Rights Rights are the quiet driver. A buyer who is unsure whether you can license a set will discount it heavily or walk away, regardless of how good the records are. Three questions decide it. What do your customer contracts and privacy notices say about using records for purposes like this? What do your employee policies say? Is any of the content licensed from someone else, such as a vendor's documentation or a partner's data, and therefore not yours to license? Most companies can license most of what they have, with specific exclusions. The value comes from knowing which, in writing, before the manifest goes out. An unresolved rights question is the most common reason a strong manifest draws a weak number, and it is the cheapest to fix. ## How the five combine A buyer reads years and volume to decide whether there is enough to learn from. Labels and modality decide how much can be learned. Rights decide whether it can be used at all. The strongest sets are deep, concentrated in a few workflows, labeled, multi-channel, and clean on rights. Most companies are strong on two or three of the five and can improve a fourth before going to market. What you cannot do is invent any of them. Do not backfill records, do not relabel history, do not guess at rights. Buyers verify at sample review, and a manifest that fails verification is worth less than an honest smaller one. ## Why pricing comes before market Because the five drivers are legible on a manifest, a set can be benchmarked against what vetted buyers are paying for comparable records before any buyer sees your name. That is how a first offer, an indicative and buyer-benchmarked range, comes back within 24 to 48 hours of a complete manifest. Binding terms follow sample review, and the range is a process target rather than a guarantee. If you want a rough sense of where your company lands, the estimator on this site takes the first three drivers as inputs and returns an illustrative range. For a real number, the manifest is the next step. **Q: Which of the five drivers matters most?** Outcome labels, in most cases. Two companies with the same years, volume, and systems can draw very different prices if one set of tickets carries resolution codes and satisfaction scores and the other only shows that messages were exchanged. Buyers are paying to learn how problems are decided and how decisions turn out, and labels are what make that learnable. Years and volume set the floor; labels and rights set how far above it the number goes. **Q: My records are in one system and only go back two years. Is it worth asking?** Probably not yet, and we will say so rather than waste your time. The qualification we use is 20 or more desk staff, three or more years of records, and data in three or more systems, which is roughly where buyers start paying. The useful move now is to keep the systems you have, stop deleting history, make sure tickets and deals are closed with an outcome, and check what your customer contracts say about data use. In a year or two the same company is a different manifest. **Q: Can I raise the value of my data before going to market?** Within limits, yes. You cannot add years, and you should never fabricate or backfill records. You can make sure what exists is exportable, that outcomes are recorded where they already happen, that transcripts exist for recorded calls, and that the rights position is documented rather than assumed. Resolving an unclear rights question with counsel before the manifest is often worth more than any other preparation, because it removes the discount a buyer applies to uncertainty. ## Blog: Red flags when someone offers to buy your company's data URL: https://datatruffle.ai/blog/red-flags-when-someone-offers-to-buy-your-data Published: 2026-11-03 Inbound offers for operational data are landing in owners' inboxes. Most are legitimate. Some are not. Six signals that separate a real buyer or agent from a bad deal, and the question that exposes each one. If your company has more than a few dozen desk staff and a few years of history, there is a reasonable chance someone has already emailed you about licensing your data. The market for operational records is young, the buyers are well funded, and a crowd of intermediaries has appeared between them and you. Most of those emails are legitimate. Some are not, and the difference is usually visible in the first exchange if you know what to look for. Here are six signals, in the order they tend to appear, each with the question that exposes it. [diagram] ## 1. Any money asked from you upfront A listing fee, a preparation fee, a valuation fee, a retainer, a fee to be introduced to buyers. The legitimate versions of this business are paid when a license closes, out of the proceeds, and not before. A buyer pays for data; an agent earns a success fee; neither needs your money to start. The question: what do I pay, and when? The only acceptable answer is nothing until you are paid. ## 2. They want the data before they give you a number A request for a database export, a bulk upload, system access, or a large sample before any price has been discussed is backwards. Buyers price from a one-page manifest, an inventory of what records exist, where, and over how long, and only later review a small de-identified sample under a signed agreement. Anyone who needs the data first is either not a serious buyer or is collecting data without paying for it. The question: can you price this from a manifest? If the answer is no, stop. ## 3. The buyers are not named Top labs. Tier-one AI companies. Our network of partners. If an intermediary cannot or will not tell you which buyers will see your manifest, you cannot evaluate the deal, and you cannot check whether the intermediary is being paid by them. Named buyers also let your counsel read the contracts behind the names. The question: which buyers, by name, before I commit to anything? ## 4. The fee is not published, or is paid by the other side Two versions. The first is an agent whose fee is per agreement and never written down until the end. The second is an introduction service that is free to you because the buyer pays it. The second sounds better and is usually worse: an intermediary paid by the buyer is working for the buyer. There are also arrangements where the intermediary is paid by both sides, which combines the problems of each. The question: who pays you, how much, and is any part of it paid by the buyer? A seller-side agent should answer with a published schedule and the word nobody. ## 5. The offer expires in days A 48-hour deadline, a price that drops if you consult anyone, a bonus for signing this week. A buyer with a strong number does not need to stop you from checking it. Deadlines like these exist to prevent the one thing that reliably raises the price of a dataset, which is a second buyer. The question: what happens if I take two weeks? If the answer is that the offer goes away, let it. ## 6. Vague on de-identification and deletion Ask what happens to customer and employee information before training, who verifies it, and when the supplied copy is deleted. A legitimate buyer has a method and will put it in the contract. A bad one says they handle all that, or uses the phrase 100 percent anonymized, which no competent party will put in writing because it is not achievable. The question: show me the de-identification clause. If there is not one, there is not a deal. ## Two more, briefly Guaranteed dollar amounts. Any figure described as guaranteed before a buyer has seen a manifest is marketing. Real offers are ranges, labeled indicative, and binding only after sample review. Pressure to sign exclusivity with the intermediary. A long exclusive engagement with an agent locks you to their buyer list and their timeline. A short, non-exclusive letter is the norm for a seller-side agent that expects to earn the mandate rather than hold it. ## Why the good ones look alike Legitimate buyers and agents converge on the same shape for a reason: the shape is what protects the data and the seller. Price from a description, not from the data. Name the parties. Publish the fee. Give the seller time to compare. Put privacy and deletion in writing. None of that is a courtesy. It is what makes the asset licensable more than once and keeps the seller out of trouble with their own customers and staff. ## Run this against us We are a new seller-side agent, and this list applies to us as much as to anyone. So here is how we answer it. No upfront fee of any kind; a success fee only, on a published schedule, paid by you out of proceeds and never by the buyer. Pricing from a one-page manifest, with a first offer within 24 to 48 hours of a complete one, indicative and buyer-benchmarked. Every buyer named before you decide anything. A two-page, non-exclusive engagement letter. De-identification and deletion in writing with every buyer, and a chain of custody in which your data never passes through us. No timelines designed to stop you from talking to anyone else. If any intermediary, including us, fails one of the six, the fix is the same: ask the question in writing, and treat a non-answer as the answer. **Q: Is it a red flag if the company that approached me is one I have never heard of?** Not on its own. Most buyers of operational data are specialist labs and data companies rather than household names, and most seller-side agents are new, including us. What matters is whether they will name the buyers they work with, publish their fee, explain their chain of custody, and give you time. An unfamiliar name that answers all four plainly is fine. A familiar name that dodges them is not. **Q: What should I do if I have already sent a sample to someone?** Ask them in writing what was shared, whether it was de-identified, who has access, and when it will be deleted, and ask for written confirmation of deletion if you decide not to proceed. Then stop sending anything further until there is a signed agreement that covers the sample. A legitimate buyer will answer quickly and confirm deletion. Treat silence or vagueness as the answer, and bring in your counsel. **Q: How do I check a seller-side agent, including you?** Run the same six questions. Is there any upfront fee or retainer? Do they want data before a number? Are the buyers named before you commit? Is the fee published and paid only by you on close, with no undisclosed payment from the buyer? Does the offer have a sensible timeline? Are de-identification and deletion in writing? Our answers are on the pricing, how-it-works, and privacy and custody pages, and we would rather you check them than take our word for it. ## Blog: De-identification, explained for owners URL: https://datatruffle.ai/blog/de-identification-explained-for-owners Published: 2026-11-01 Before a buyer trains on your records, the people in them are removed. Here is what that means in practice, what a de-identified ticket looks like next to the original, and what belongs in the contract. When an owner hears that an AI company wants to train on their support tickets, the first question is usually about the people in them. Customers who wrote in with a problem. Employees whose names are on every reply. Suppliers copied on a thread. None of them signed up to appear in a training set, and no owner wants to be the reason they did. De-identification is the answer to that question, and it is less mysterious than it sounds. It is the process of removing or replacing the information that identifies a person before a buyer uses the records, and it is written into the contract rather than left to goodwill. Here is what it means in practice. [diagram] ## Before and after, on one ticket Here is an illustrative support ticket as it might sit in a helpdesk today. Every detail is invented. > **From:** Dana Whitfield, dana.whitfield@example-client.com, +1 415 555 0142 > > **Subject:** Invoice 48213 charged twice > > **Body:** Hi, we were billed twice for the March renewal on account AC-7781. Can you reverse one? Our CFO Mark needs this fixed before the board meeting on the 14th. > > **Agent:** Priya Raman > > **Resolution:** Duplicate charge confirmed, refund issued, root cause was a retry in the payment gateway. The same ticket after de-identification: > **From:** [CUSTOMER_0412], [EMAIL], [PHONE] > > **Subject:** Invoice [INVOICE_ID] charged twice > > **Body:** Hi, we were billed twice for the [MONTH] renewal on account [ACCOUNT_ID]. Can you reverse one? Our CFO [PERSON] needs this fixed before the board meeting on the [DATE]. > > **Agent:** [AGENT_07] > > **Resolution:** Duplicate charge confirmed, refund issued, root cause was a retry in the payment gateway. Everything a model learns from is still there: a billing problem, a clear request, an urgency signal, a diagnosis, and an outcome. Everything that points to a person is gone. Tokens like [CUSTOMER_0412] stay consistent across that customer's tickets, so the buyer can still see a history without knowing whose it is. ## What gets removed **Direct identifiers.** Names, email addresses, phone numbers, account and invoice numbers, physical addresses, payment details, employee IDs. These are replaced with consistent tokens or dropped entirely. **Quasi-identifiers.** Information that is harmless alone and revealing in combination: exact dates, small locations, rare job titles, the names of customer companies. These are generalized. A date becomes a month or is shifted by a fixed offset; a town becomes a region; the only pediatric dentist in a small town becomes a dental practice. **Free text.** The hard part. Names and numbers appear inside sentences, signatures, quoted replies, and attachments, not just in fields. This step uses pattern matching plus models trained to spot personal information in prose, followed by a human review of a sample. **Attachments and metadata.** Documents carry author fields and tracked changes; images carry location data; calendar invites carry attendee lists. Either the attachment is excluded or it goes through the same pass. ## Who does the work The seller-side agent does not, ever. The agent never receives the dataset, so it cannot process it. That is a structural fact about chain of custody, not a policy. In most licenses, the buyer de-identifies under contract before any training run, using tooling built for this. Some owners prefer to run a first pass on the export themselves, with their IT team or a vendor, so that what leaves the building is already reduced. Both are legitimate. The contract should say which, and should name the method. ## What de-identified does not mean It does not mean perfect. No method reduces re-identification risk to zero, and you should be suspicious of anyone who writes 100 percent anonymized into a proposal. It means the direct identifiers are gone, the quasi-identifiers are generalized to an agreed standard, and the buyer is contractually barred from trying to reverse it. It also does not automatically cover employees. The reported Spirit Airlines data sale drew an objection from a pilots' union on exactly this point: the privacy terms, by the union's reported account, protected passengers more clearly than staff. Whatever the court decides, the lesson for an owner is simple. Ask whether the de-identification standard applies to the people who wrote the records, not only the people they wrote about. ## What belongs in the contract A workable de-identification clause has six parts. Ask for all of them. 1. **Method.** What is removed, what is generalized, what tooling is used, and whether tokens are consistent. 2. **Scope.** Customers, employees, and third parties alike. 3. **Verification.** Who checks a sample before training, and what failure rate sends it back. 4. **Prohibition.** No attempt to re-identify, by the buyer or by anyone it shares derived work with. 5. **Deletion.** A date by which the supplied copy is destroyed, and written confirmation when it is. 6. **Audit and notice.** A right to confirm the above, and a short deadline for telling you if anything goes wrong. Every buyer we work with contracts to de-identify records before use and to put these terms in writing. We will not introduce a buyer who will not. ## The practical sequence Nothing is de-identified at the start, because nothing moves at the start. Buyers price from a one-page manifest that contains no records. After a first offer, the buyers you approve see a small sample, which is de-identified before it is shared and is covered by a signed agreement. Only after binding terms and your signature does the dataset move, directly from your systems to that buyer, with the method and the deletion date already fixed. If you want to see the standard before you decide anything, the privacy and custody page sets it out in full. The manifest is still the place to start, and it is the only thing that leaves your building before you sign. **Q: Who actually does the de-identification work?** In most licenses the buyer does it, under contract, before any training run, using tooling built for exactly this. Some owners prefer to run a first pass themselves on the export, with their own IT team or a vendor, so that what leaves the building is already reduced. Either way the method, the verification step, and the deletion date are written into the agreement. A seller-side agent never performs it, because the agent never holds the data. **Q: Can de-identified records ever be traced back to a person?** The honest answer is that no method reduces the risk to zero, which is why no serious buyer or agent will put the words one hundred percent in writing. Direct identifiers can be removed completely; quasi-identifiers like dates, locations, and unusual job titles are generalized so that combinations no longer point at one person. The contract then adds a prohibition on attempting re-identification, an audit right, and breach notice. Ask any buyer to describe their method and their verification step. Vagueness there is a red flag. **Q: Does de-identification remove the value buyers are paying for?** Mostly no. Buyers want the structure of the work: the problem, the questions asked, the decision, the outcome. None of that depends on knowing the customer's name or phone number. What can reduce value is over-aggressive scrubbing of the free text, which is why the method matters and why a sample is reviewed before terms are final. A good de-identification pass removes who while keeping what happened. ## Blog: The case for competing offers URL: https://datatruffle.ai/blog/the-case-for-competing-offers Published: 2026-10-31 One buyer names one number. Several buyers reveal what the records are worth. A widely reported 2026 bankruptcy auction shows the gap, and the same logic applies to a company that is not for sale. The best public illustration of why competing offers matter did not come from a data company. It came from a bankruptcy court. In 2026 the business press reported on the sale of the data assets of the former Spirit Airlines: by the reports, roughly 100 million emails, 500 million chat messages, and 30 million lines of code. Google was reported to have opened the auction at $5 million. After several rounds, the airline reportedly selected Google's $10 million bid, with the AI data company Mercor named as the alternate at $7.5 million. Days later, another AI data company, micro1, was reported to have submitted a late offer of $12.5 million, with the outcome left to the court. Reported figures, reported sequence, and unsettled when this was written. But the shape is the thing to notice: the same records, described once, moved from $5 million to a reported $12.5 million because more than one buyer wanted them on the same deadline. [diagram] ## One buyer prices for the buyer When a single buyer approaches a company about its records, the number in the email is not a valuation. It is the amount that buyer has decided is worth paying for this category of data this quarter, discounted for the chance that the owner will say yes without asking anyone else. That is not dishonest. It is how any buyer behaves when the seller has no reference price. The problem sits on the seller's side: most owners have never licensed data before, do not know who else buys it, and have no way to tell whether the number is generous or a third of what the records would draw elsewhere. A single inbound offer therefore carries two unknowns. Is the price right, and are the terms right. Without a second buyer, neither can be tested. ## What competition changes Price is the obvious part, and the reported auction shows how far it can move. The less obvious part is terms, which in data licensing often matter more than the headline figure. **Exclusivity.** A buyer who is the only bidder asks for exclusive rights by default, because exclusivity costs nothing to request. A buyer competing with two others will often accept non-exclusive terms, which means the same records can be licensed again to a buyer in a different field. **Field of use.** One buyer wants the records to train a customer-service model; another wants them for evaluation only. Splitting the use between them is possible only when both are at the table. **Term and deletion.** The length of the license, and the date by which the supplied copy is deleted, are negotiable when there is a second buyer and fixed when there is not. **Payment timing.** Competing buyers tend to shorten their payment terms. A sole buyer tends to lengthen them. Each of those moves the real value of the deal to the owner, and none of them appears in a one-line inbound offer. ## Why most owners do not run one Three reasons come up every time. They do not know the buyers. The companies that license operational data are not household names. There are frontier labs, specialist data labs, model builders, and enterprise AI teams, each with its own programs, and the list changes every quarter. They do not have the time. Running a process means describing the data once, approaching several buyers, handling questions, and holding everyone to one deadline. A busy owner who gets one offer on a Monday is tempted to decide by Friday. They are worried about exposure. The instinct is that talking to five buyers means five companies looking at the data. It does not, if the process is run correctly. ## Competing without exposing anything A competitive process is run from a manifest, the one-page inventory that describes the records without containing any. Every buyer that fits gets the same page, on the same day, with the same deadline. They price from that page. None of them sees a record. Only after a first offer comes back, and only for the buyers you approve, does a small de-identified sample follow, under a signed agreement that governs its use. The dataset itself stays in your systems until a license is signed and then moves directly to the buyer you chose. At no point does it pass through the agent running the process. Done this way, competition costs the owner an afternoon to approve the manifest and a few decisions along the way. The buyers do the work of competing. ## The deadline that is really a red flag One tactic is worth naming because it is designed to prevent exactly this. An offer that expires in 48 hours, or that must be accepted before anyone else is consulted, is not an offer with a strong price behind it. A buyer confident in their number does not need to stop the seller from checking it. Treat the deadline as information, and take the two weeks anyway. ## What we do with this Our process starts by pricing the manifest against what vetted buyers are paying for comparable records right now, before any buyer sees your name. That produces a first offer, an indicative and buyer-benchmarked range, within 24 to 48 hours of a complete manifest. Then the qualified set goes to more than one buyer at once, with one deadline, and binding terms follow sample review. We think the right number of offers is three, not one, and the reported auction above is the plainest public case for why. If a buyer has already approached you, the manifest is still the first step. It tells you what the number should have been. **Q: Will shopping an offer to other buyers offend the buyer who approached me?** A serious buyer expects it. Data licensing teams run competitive processes all the time from the other side, and a seller who asks for two weeks to benchmark is not a surprise. A buyer who reacts to that request by shortening the deadline or withdrawing is telling you something about the offer. The polite version is simple: you are interested, you want to understand the market value, and you will come back with a decision on a date you name. **Q: Does a competitive process expose my data to more companies?** Not if it is run from a manifest. Buyers price from a one-page inventory that contains no records, and only the buyers you approve ever see a small de-identified sample, after a signed agreement covering its use. Competition happens on the manifest and on terms, not on the dataset. Your data never passes through the agent and moves only to the buyer you sign with, after signing. **Q: What if only one buyer is interested?** Then you still want to know the benchmark before you answer. Pricing the manifest against what vetted buyers are currently paying for comparable records tells you whether the one offer is fair, low, or unusually high, and gives you a number to negotiate from. A single bidder is not a reason to accept the first number. It is a reason to make sure the first number was priced from the market rather than from the buyer's budget. ## Blog: What a one-page data manifest is, and why buyers price from it URL: https://datatruffle.ai/blog/what-a-one-page-data-manifest-is Published: 2026-10-29 A manifest is an inventory, not a dataset: what records exist, in which systems, over how many years, with what rights. It is the page buyers price from, and the reason you never send data to get a number. Owners who get an inbound offer for their data usually assume the next step is to send some of it. It is not. The document that moves first, and the one buyers actually price from, is a single page that describes the data without containing any of it. That page is the manifest. A manifest is an inventory. It says what records exist, in which systems, over how many years, in what volume, and under what rights. It does not include a single customer name, email address, or ticket. It is closer to the schedule of assets in a sale agreement than to a data room. If you can describe your company's systems to a new IT hire, you can produce one. [diagram] ## What is on the page A complete manifest answers eight questions, usually as eight rows of a table. **Systems.** Which platforms hold the records: the helpdesk, the CRM, the email tenant, the chat tool, the document store, the billing system, the call platform, the engineering tracker. Named by product, because buyers know what each export looks like. **Record types.** What each system holds: tickets with resolution notes, deals with stage history, threads, recordings with transcripts, proposals, invoices and disputes. **Date range.** The earliest and latest records that still exist, per system. Migrations matter here. If the helpdesk moved in 2021 and the old data was not carried over, say so. **Volume.** Approximate counts: tickets, messages, documents, hours of calls, closed deals. Round numbers are fine. **Format.** Whether a native export exists, whether attachments are included, whether transcripts already exist for audio. **Outcome signals.** Whether records carry results: resolution codes, satisfaction scores, closed-won and closed-lost, dispute outcomes, merged or rejected. This row changes the price more than any other. **People and rights.** Who appears in the records (employees, customers, third parties), what your contracts and notices say about use of that data, and whether any of it is licensed from someone else. **Exclusions.** What you will not license: privileged material, regulated categories, a specific customer, a specific period. That is the whole document. Eight rows, one page, no data. ## Why buyers price from it Buyers price from a manifest for the same reason a lender prices from a rent roll rather than walking every unit. The manifest carries the variables that drive value, and a buyer who has read many of them can benchmark a new one quickly. Depth and continuity come from the date range and volume. Learnability comes from the outcome signals and the record types. Usability comes from format and rights. A buyer building a customer-service model this quarter reads the ticket row first; a buyer working on sales agents reads the CRM row. The same page is priced differently by different buyers, which is exactly why it should go to more than one. The manifest also lets a buyer say no early, which is a feature. If the rights row shows that half the records belong to a customer under a contract that forbids reuse, the buyer learns it on page one instead of after a sample, and no one has wasted a month. ## What a manifest is not It is not a dataset. Nothing in it can identify a person. It is not a sample. Samples come later, are small, are de-identified, and are approved by you by category or record by record. It is not a data room. There is no upload, no shared drive, no connector. The manifest is a document you write and approve, and it is the only thing that passes through a seller-side agent. It is also not a promise. A manifest describes; diligence verifies. A first offer based on a manifest is indicative, and binding terms follow sample review. ## Building one in an afternoon Start with the systems list. Walk through the tools your team logs into every day and write down each one. Ask the administrator of each for two numbers: how far back the data goes and roughly how much there is. Note which systems have been migrated and what was lost. Then mark the outcome signals. For each system, ask whether a record shows how things ended. Tickets with resolution codes, deals with stages, disputes with rulings. If a system only shows activity, say so. It still has value, just less. Finally, read the rights row with your counsel. Customer agreements, employee policies, privacy notices, and any content you license from a vendor. Most companies find they can license most of what they have, with specific exclusions. Write the exclusions down. A manifest that honestly says six years, 40,000 tickets, resolution codes on most of them, and two excluded customers is worth more than one that claims everything and survives nothing. ## What makes a manifest weak Vague systems rows, such as the single word email with no tenant, range, or volume. Volumes that do not survive a sample. A rights row left blank. Outcome signals left unmentioned when they exist. A single line for ten years of data that actually lives in three systems with two migrations between them. Weak manifests do not produce low offers so much as slow ones, because every buyer has to ask the same questions before pricing, and some will not bother. ## From manifest to offer Once the page is complete, it is benchmarked against what vetted buyers are paying for comparable records right now. That is what makes a first offer in 24 to 48 hours possible: the pricing work happens before any buyer sees your name, and the buyers see a manifest, not a dataset. The offer that comes back is a range, labeled as indicative, with binding terms to follow after the buyer reviews a sample you approve. If you want to know what your records are worth, the manifest is the first step, and the only document you will need to produce to get a number. **Q: Do I have to give anyone access to my systems to build a manifest?** No. A manifest is written from what you already know about your own company: which systems you use, roughly how many records each holds, how far back they go, and who appears in them. Most of it comes from an afternoon with whoever administers your email, helpdesk, CRM, and file storage. Nobody connects to anything. The dataset stays where it is until a contract is signed, and then it moves directly to the buyer. **Q: How accurate do the numbers on a manifest need to be?** Accurate enough to be honest, not exact. Round figures are normal: about 40,000 tickets, roughly six years, a few hundred recorded calls a month. Buyers expect approximations at this stage and verify them during diligence against a sample. What hurts is overstating. A manifest that claims ten years when a helpdesk migration kept only the last four will be found out at sample review, and the offer will move down. **Q: What happens after the manifest is approved?** The manifest is benchmarked against what vetted buyers are currently paying for comparable records, and a first offer comes back as an indicative, buyer-benchmarked range within 24 to 48 hours. If you want to proceed, buyers review a small de-identified sample you approve, complete diligence, and return binding terms covering price, exclusivity, field of use, term, and deletion. You sign with the buyer you choose. The first offer is a process target, not a guarantee. ## Blog: Why AI labs pay for operational records when the public web is exhausted URL: https://datatruffle.ai/blog/why-ai-labs-pay-for-operational-records Published: 2026-10-28 Models have read the public internet. What they have not seen is how a real company runs. That gap is why buyers now license operational records, and why yours may be worth more than you think. Every frontier model has read the public internet. The articles, the forums, the documentation, the open-source code: that material has been collected, cleaned, and trained on many times over. The research group Epoch AI has published a widely reported projection that the stock of public, human-written text could be fully used by training runs between 2026 and 2032. The exact year is debatable. The direction is not. What no crawler has ever seen is how a real company runs. Not what it says on its website, but what happens when a request comes in on a Tuesday afternoon, who decides, what the exception was, and how it turned out. That knowledge sits in support desks, inboxes, chat channels, and CRMs inside established businesses. It is the one large body of human work that is both unread and still being produced. That is why buyers now pay to license it. [diagram] ## The public web shows what companies say A marketing page is the clean, finished version of a company. A press release is the version the company wants remembered. Even a detailed knowledge base describes the policy, not the practice. All of it is useful to a model, and all of it has already been learned. What that material lacks is the middle of the work. A refund policy says refunds are issued within ten days. It does not show the handful of cases where the policy was overridden, the reason a manager approved the override, or the message the agent wrote afterwards. A model that has read every policy on the internet still does not know how a policy is applied by people under time pressure. ## What a model learns from a resolved ticket Take one support ticket from a company that has run the same desk for six years. The customer describes a problem in their own words. An agent asks two clarifying questions. The issue is tagged, routed, escalated once, and closed with a resolution note. Later, a satisfaction score arrives. That single record contains a problem statement, a diagnostic conversation, a decision, and an outcome. Multiply it by tens of thousands of tickets and you have something the public web cannot provide: a dense, labeled record of how problems are actually solved in one domain, with the failures included. Buyers describe this as workflow data, decision data, or outcome-labeled data. The plain version is that it shows work being done rather than described. The same is true of a sales pipeline with closed-won and closed-lost outcomes, an engineering tracker with the review discussion attached, or a billing system that shows which disputes were upheld. The common thread is that a human made a judgement, and the record shows what happened next. ## Why the buyers changed Three things happened at roughly the same time. First, the free supply thinned. Labs have been open about the fact that high-quality public text is a finite resource, and more of what remains online is itself model-generated, which is of limited use for training the next model. Second, licensing became normal. Reuters reported in 2024 that Reddit had agreed to license its content to Google for roughly $60 million a year. News publishers signed reported multi-year agreements with model builders. Paying for a source stopped being unusual. Third, the market reached operational data. In 2026 the business press reported a bankruptcy auction for the data assets of the former Spirit Airlines, described in reports as roughly 100 million emails, 500 million chat messages, and 30 million lines of code. The opening bid was reported at $5 million; competing bids from Google and AI data companies were reported well above that, subject to court approval. The point is not the airline. The point is that a company's ordinary working records were treated as a distinct asset with a market price, and more than one buyer showed up. ## What operational means in practice The records buyers ask about are not exotic. Email and calendar. Documents, proposals, and standard operating procedures. Team chat. Recorded calls, where consent exists. CRM activity and deal notes. Support and service tickets. Billing, disputes, and collections. Engineering workflows, including the discussion around code review. You do not need to be a technology company. A regional accounting firm, a logistics operator, a property manager, or a specialty manufacturer has all of this. What matters is that the same workflows have run in the same systems for a few years, so the records are consistent and deep enough to learn from. Buyers are grouped by what they build rather than by name: frontier labs, specialist data labs, model builders, and enterprise AI teams. Each wants a slightly different slice, which is why the same records can draw different numbers from different buyers. ## What this means for an owner A few practical consequences follow. Your records are an asset you already own, and no one else can produce them. A competitor cannot generate your six years of tickets, and a lab cannot crawl them. The value depends on depth and continuity more than on industry. Our qualification is 20 or more desk staff, three or more years of records, and data in three or more systems. Companies below that line are not turned away; they are told what to start documenting so the records are worth something later. You do not have to send anyone data to find out what it is worth. Buyers price from a one-page manifest, an inventory of what exists and where, and later from a small de-identified sample you approve. The dataset itself stays in your systems until a contract is signed, and then moves directly to the buyer, never through an intermediary. And the price is not fixed. The same manifest can draw very different numbers from different buyers, depending on what each one is building this quarter. That is the case for pricing the records before they go to market, and for running a competitive process rather than accepting the first inbound number. The public web was the first supply. Operational records are the second, and they belong to the companies that produced them. If you want to know what yours are worth, start with the manifest. A first offer follows within 24 to 48 hours of a complete one. It is indicative and buyer-benchmarked, and binding terms follow sample review. **Q: Why would an AI lab want records from a company that has nothing to do with AI?** Because the records show work being done, not described. A model trained on public text knows what a refund policy says. A model trained on five years of support tickets knows how refunds are actually decided, escalated, and closed, including the exceptions. Buyers pay for that second kind of knowledge because it is the part the public web cannot teach, and it exists only inside companies that have run the same workflows for years. **Q: Is the public web really running out?** The widely reported projection from the research group Epoch AI is that the stock of public, human-written text could be fully used by frontier training runs somewhere between 2026 and 2032. Whether the exact year is right matters less than the direction. The easy, free supply is finite, more of what remains online is itself generated by models, and labs have responded by paying for licensed sources. Reported deals with publishers and forums are the public evidence of that shift. **Q: Does this mean every company's data is valuable?** No. Buyers want depth and continuity: a few years of history, enough volume to learn from, and records that show outcomes rather than just activity. The qualification we use is 20 or more desk staff, three or more years of records, and data in three or more systems. Below that line, the right move is usually to start documenting consistently now so the records are worth something in a couple of years.