Frontier labs need to see how work actually gets done: how teams decide, escalate and handle the cases that don't fit the rule. That doesn't exist on the open web, and synthetic data can't reproduce it.
Enterprise Data Partnerships
Enterprise Data Partnerships
Your data doesn't just run your business. It can build AI, and build revenue.
Frontier labs need to learn how work actually gets done inside a company. We source that data with your consent and pay you for it, handling the permissions, sanitization and structuring in between.
What we're looking for
The data that shows how the work happened, not just what it produced.
Frontier models have already learned everything the open web can teach. The next gains don't come from more data, they come from the work that never gets published: the drafts, the decisions, the exceptions that live inside a working company.
We're asking for the work in motion: the versions, the decisions, the conversations that got you there. Not a database export. That's what's scarce, and that's what we pay for.
By type of data
01
Code & internal tooling
Repositories, review history, scripts and configuration. The record of how software actually got built and changed.
GitHub · GitLab · Bitbucket
02
Structured & tabular data
Transactional records, master data and time series, anonymized before they leave your environment.
Snowflake · BigQuery · ERP systems
03
Policies, processes & procedures
SOPs, playbooks, runbooks and control frameworks. The documented version of how work is supposed to go.
Confluence · Notion · SharePoint
04
Business documents
Models, decks, memos, reports and specs, including the earlier drafts and not just the final file.
Drive · SharePoint · Box
05
Internal communications
Approval, review and dispute threads, where the actual reasoning happens. Scoped to what you opt in.
Slack · Teams · Email
06
Regulated documents
Filings, statutory records, audit and compliance workpapers, under a vertical-specific legal framework.
Licensed per vertical
What makes a dataset valuable
It isn't your industry. It's whether the history survived.
A folder of finished files is worth very little, because a model can't learn anything from an outcome with no path to it. What labs pay for is the trail: how the work moved from a blank page to a decision.
01
The history is intact
Earlier versions and iterations still exist, not just the final file. A v1 through v6 is worth far more than a polished v6 on its own.
02
The conversation survived
The approvals, reviews and disagreements around the work are still somewhere: a channel, a thread, a comment history.
03
There's a documented process
An SOP, a policy or a playbook that says how this work is supposed to go, so the gap between rule and judgment becomes visible.
04
It's genuinely proprietary
It isn't published, scraped or reproducible from public sources, and synthetic data can't stand in for it.
We're open to every industry. Any function that produces documented work at volume is worth a conversation, whatever sector it sits in.
How it works
Four steps, from first call to payment.
You don't need a process map or a clean data room to start. Most companies begin with one function and one workflow, and we do the heavy lifting from there.
1
Discovery
We map which business functions you operate and where that work already lives.
2
Scoping
Together we define what's in scope, what's excluded, and how it gets anonymized. All agreed in writing before anything moves.
3
Collection & sanitization
We handle extraction, PII removal and structuring. You review a sample before anything is delivered.
4
Delivery & payment
The dataset goes to the lab under confidentiality. You're paid on agreed milestones.
You don't have to start deep. Most partnerships begin with documented knowledge or a redaction pilot, with no live system access, so you can see how we work before widening the scope.
How your data is handled
You decide what's shared, and on what terms.
You approve what's shared
Scope, exclusions and anonymization rules are agreed before any transfer. Never a blanket export.
Anonymized before delivery
How much is removed, and how, depends on the data and how sensitive it is. We agree the approach with you during scoping, and you see a sample of the prepared dataset before anything is delivered.
Provenance travels with it
Consent basis, usage restrictions and deletion terms are attached to the data itself, not just described in a contract.
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Frequently Asked Questions
It depends on the scope: the volume, the depth of history, and how hard the dataset would be to source anywhere else. It varies enough that we won't quote a number before understanding what you have, and whatever we agree is put in writing before any data moves.
You approve the scope before anything is collected. Identifying detail is removed before delivery, and on our side access is limited to the team running the pipeline. The lab receives the prepared dataset, not a window into your systems.
Raise it early and we work through it with your legal and compliance teams during scoping. Anything that can't be resolved stays out of scope. We would rather narrow the dataset than build one you can't defend.
Don't reorganize it for us. Renaming files or restructuring folders strips out the trail we're looking for, so hand it over as it sits, history included. What does matter is the discipline that was already there: versions kept, decisions written down, a process documented while the work happened. We're not asking you to build that now, only not to undo it. Extraction, PII removal and structuring for delivery are on us.
Payment is tied to delivery milestones, and the terms are agreed in writing before any data moves. In practice that means the commercial side is settled during scoping, not after you've handed anything over.
Still have questions? Talk to us
Turn the work you've already done into revenue.
You don't have to change how anyone works, because the record already exists. What's missing is the permission layer, the anonymization and a buyer, and that's the part we handle.