Data opportunities
Your quotes, investigations, and inspection records capture years of expert decisions. Epistemic Labs turns that history into documented datasets and brokers licenses to AI labs and model companies. You keep ownership.
01What AI companies buy
Expert decisions linked to real outcomes give AI teams examples to train on and cases to test against. Quotes won or lost, repairs that held, and investigations that resolved a problem capture how work gets done.
A CRM export does not establish value by its row count.
The useful structure is the chain inside it: a customer inquiry, the salesperson's response, the follow-ups, and whether the customer bought and stayed. The same shape hides in support desks, ERPs, inspection apps, and claims systems.
CRM
Outcome: Purchase or not, then retention
Support platform
Outcome: Customer outcome
ERP and email
Outcome: Purchase or loss
Field inspection app
Outcome: Repair outcome
Claims system
Outcome: Dispute outcome
Linked outcomes can make decisions useful for AI.
02The distinction that matters
| Lots of data | A dataset AI can learn from |
|---|---|
| Millions of rows | Expert decisions linked to what happened next |
| Exports and table dumps | Complete chains, from request to resolution |
| Logs with no outcome attached | Corrections, overrides, and second opinions on record |
| Data anyone could generate in a week | Rare edge cases, failures, and disputes |
| A recent snapshot | Years of linked decisions and outcomes |
Record volume alone does not establish value. The task is to find these links and test their usefulness to a buyer.
03Where it lives
Quotes, claims files, and dispatch logs can hold decisions and outcomes that are hard to reproduce. We look for those links, not volume alone.
01
CRM
Salesforce, HubSpot, Dynamics
02
ERP
Orders, inventory, production
03
Accounting
Ledgers, invoices, reconciliations
04
Support tickets
Zendesk, Freshdesk, ServiceNow
05
Email archives
Customer and vendor correspondence
06
Recorded calls
Sales, support, dispatch
07
Documents
Contracts, reports, filings, specs
08
Images & video
Inspections, claims, site photos
09
Industry software
TMS, LIMS, MES, practice management
10
Operational logs
Events, telemetry, machine data
11
Human annotations
Tags, reviews, QA scores
12
Historical decisions
Approvals, rulings, dispositions
04How it works
We assess the records, build a documented package, and approach AI teams with a relevant technical need. You approve each buyer and the terms.
01
Find records of expert decisions and their outcomes.
02
Assess quality, scarcity, scale, and fit for AI tasks.
03
Define the extract, document its fields, and prepare a sample under NDA.
04
Approach AI labs, model companies, and enterprise AI teams with a real need.
05
License fee, permitted uses, exclusivity, and term, negotiated with your approval.
06
Sign the license and deliver the agreed dataset.
05Preliminary valuation
Describe your systems and the decisions they record. In about five minutes, see your data opportunity score, a preliminary value range where supported, and the factors behind the result.
What we ask about
Data opportunity score
82/ 100
Strong signals
To evaluate
06A clear licensing process
07Illustrative opportunities
Illustrative composites showing how quotes, investigations, and expert classifications become training and evaluation examples.
Quote requests, salesperson responses, negotiation threads, and final purchase or loss records linked over time.
Decision chain
Potential AI uses
Support tickets with agent investigation notes, internal escalations, tool actions, and final resolutions.
Decision chain
Potential AI uses
Inspection photos paired with technician classifications, severity ratings, and the repair that followed.
Decision chain
Potential AI uses
Candidate histories, recruiter screening decisions, interview progression, and hiring outcomes.
Decision chain
Potential AI uses
08Industries
Quotes, investigations, classifications, and their outcomes record the expertise inside established businesses.
For AI buyers
Describe the decisions, outcomes, or workflows your models need. We find businesses whose records match the task and help define the dataset, documentation, and license.
Quotes linked to purchases, tickets linked to resolutions, inspections linked to repairs, and claims linked to decisions. Expert reasoning, corrections, tool actions, and outcome history give AI teams examples for training and evaluation.
We act as a broker and advisor. Buyers license the dataset directly from you on terms you approve.
We earn a brokerage fee only when a transaction closes. The fee is agreed with you in writing before we approach any buyer. The preliminary assessment is free.
Start with dataset descriptions: the systems you use, the history you hold, and the decisions and outcomes recorded. After a mutual NDA, we review schemas and a sample through an agreed diligence channel, then prepare documentation for buyers you approve.
The preliminary assessment takes about five minutes. The next stages are dataset preparation, buyer review, and negotiation. We agree the steps and schedule with you after reviewing the dataset.
You choose the terms. Licenses can be non-exclusive, exclusive for a field or a period, or limited to uses such as evaluation. We help you weigh pricing, duration, and future licensing options.
We help define the extract, connect decisions to outcomes, document the fields, and prepare a sample. Your engineer or analyst works with us on the source systems and approves the package.
Start here
Describe your systems in about five minutes. See your data opportunity score, a preliminary range where supported, and the factors that make your records useful to AI teams.