Evaluate AI agents without your data leaving your tenant
Proofs of concept stall because vendors need real data to prove their model works, and nobody wants to hand sensitive files to a vendor to find out.
The usual bottleneck
Comparing AI tools means giving each vendor a sample of real data, waiting on legal and security review, and then comparing results that were produced in different places under different rules. Most of the delay in a proof of concept is this handoff, not the model.
Bring the agent to the data instead
Run the comparison inside your own environment. A vendor's agent is invited in for one job: it is given a scoped, time-limited way to reach one file, it does its work, and it returns a result. Nothing is copied out permanently, and the agent only ever sees what that one job needs.
Because every vendor works against the same data under the same boundary, you can run several agents side by side and compare like for like, instead of judging each one from a different sample and a different trust arrangement.
What this looks like in practice
- OCR engines compared on the same scanned documents
- Summarization models compared on the same long documents
- Entity extraction tools compared on the same annotated records
- Imaging models evaluated on the same scans, without the scans ever leaving your subscription
Why it holds up under audit
Every job is scoped to one agent, one file, and a limited window, and every call is logged. That turns a proof of concept from an informal trial into something compliance can actually review afterward.
This is the Evaluation harness at work, with Security setting the boundary and Audit keeping the record.