AI agents need records of real work, not just more text. Companies holding that history can now license it. SourceX offers up to $4M to those that qualify.
The next source of AI data isn’t public. It’s inside companies.
Ask an AI how refunds work and you’ll get a polished answer. Ask it to issue the right refund to a customer with an old contract, an earlier credit and a failed payment attempt, and things get hard fast.
What did the agent know? What was it allowed to do? What did the system actually record?
That gap, between describing work and doing it, is where the next phase of AI is being fought. The data needed to close it isn’t on the open web.
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Why do AI agents need enterprise workflow data?
Enterprise workflow data captures the request, rules, actions and outcomes of real work. For AI agents, it can support training and evaluation tasks that demand more than a fluent answer. Its value depends on context, clear timelines, evidence of results and permission for the intended use.
The next source isn’t on the open web
For years, models learned mostly from public text. According to SourceX, much of that openly available text has already been used, and AI developers are looking for new sources of real-world data, preferably licensed with the owner’s agreement.
Public text teaches a model to talk about work. It rarely shows the work itself: the ticket that took three tries, the exception that needed sign-off, the fix that actually held. That record lives in the CRMs, help desks and trackers of ordinary companies.
SourceX is betting on that supply. It licenses business data directly from companies and offers qualifying ones up to $4M.
Talking isn’t doing
The original 2024 tau-bench study tested tool-using agents by checking whether the database ended up in the right state, not whether the answer sounded right. The tested agents, including GPT-4o, completed fewer than half the tasks. Those are historical results, not a verdict on today’s systems, but the lesson stands: a confident sentence doesn’t prove that a payment was posted.
Real business systems record that difference. That is why they matter.
What makes a company’s history useful
Take an illustrative invoice dispute. The CRM holds the relationship. The document store holds the agreement. Support holds the complaint. Finance holds the adjustment. Together, they show how the company reached a result.
Exported separately, they lose the links. Worse, a later note can leak the answer to a question asked earlier in the workflow. Stable identifiers, versions and timestamps keep the sequence honest.
Define the unit (a message, a case or a full episode) and keep related records from slipping across the training and testing split.
“Closed” can mean anything
A case marked closed might be a verified fix, a customer who gave up or an administrative cleanup. Only someone who knows the workflow can tell you which, and those people don’t stay forever.
Status fields that look consistent for years can hide shifting definitions. An archive of retained successes can’t represent the whole operation. Documenting that isn’t just good hygiene. It is what lets a buyer price a collection instead of discounting an unexplained pile of records.
Rights first
Owning a system doesn’t mean owning everything in it. Customer attachments and third-party content may carry restrictions, and technical access isn’t permission to license data for AI.
Identify the current rights holder, the selected records and the proposed use. The legal scope may be narrower than the technical export. That’s fine; a clearly bounded collection is easier to assess.
Who gets paid in the data rush
The best-placed companies are often the ones that never thought of themselves as data companies. SourceX’s best-fit suppliers generally reached 50 or more full-time employees at peak, have several years of history and are in a supported location. Operating, acquired and wound-down companies can all qualify.
The first step uses company information only, with no raw uploads. If there’s a fit, SourceX handles agreements, rights review, preparation and payout, and the company approves scope and release. Standard terms are a one-time fee for perpetual AI-training exclusivity; 12- or 24-month terms and non-exclusive licenses can be agreed, the latter at roughly a quarter of the exclusive range.
Applying creates no commitment. Waiting has a cost: every departure and system retirement makes your history harder to explain, and harder to license.
The fine print: Up to $4M is SourceX’s advertised maximum, not an estimate for any company. Actual offers depend on eligibility, rights, data quality and buyer demand, and not every company or collection qualifies; unselected data earns no payment. Customer-owned and third-party material may need separate treatment, and retention, deletion and legal-hold obligations always apply. Signed terms govern.
Questions business leaders ask
Is private business data automatically better for AI?
No. A collection must support a defined task with useful context, suitable quality and permitted use. Being private doesn’t establish value on its own.
Why do timestamps matter for AI evaluation?
They separate what was known before a decision from evidence recorded afterward. Putting later outcomes into an earlier task’s input leaks the answer.
Can a business explore licensing without committing its archive?
Yes. SourceX’s first company fit check requires no raw uploads. Any later transaction proceeds through defined scope, rights review and agreed terms.
Lead a company with years of support, CRM or engineering history? Check its fit with SourceX while the people who can explain it are still on the team.
About the Founder
Noah Loul is a four-time founder and the founder of SourceX, which licenses business data directly from companies for AI training and evaluation. His ambition is to build the world’s largest data infrastructure, powering today’s AI and tomorrow’s superintelligence.
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