# 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)