Bring us the data, the workflow, or the system that isn't working yet. We'll tell you what we'd build and why, then build it. The people who scope the work are the people who ship it, not a separate team it gets handed to afterward.
Purpose-built tooling for teams that produce or consume human data: systems a generic labeling platform can't give you, because it wasn't built for your task.
Retrieval systems, document intelligence, and agentic workflows engineered inside your environment: AWS, Azure, or GCP. Every system leaves our hands with its own test suite: defined success criteria, scored behavior, and the instrumentation to keep measuring after launch.
We won't ship what we can't score. It's a slower first week and a much better sixth month.
Structured, repeatable testing of AI systems before they carry real weight: whether you built them, or a vendor did. Expert-graded against criteria drawn from your actual workflow, with adversarial probing where the risk warrants it.
Because we didn't write the system and don't need the remediation contract, the findings are worth taking to a board, a risk committee, or the vendor who shipped it.
The two practices sharpen each other. The data work funds the judgment; the judgment disciplines the build.
Engineers who spend part of their week scoring model outputs write different software: testable behavior, honest failure modes, instrumentation nobody has to retrofit. And data operations run better on tooling built by people who have personally sat in the rater's seat.
The lowest-friction way in: a two-week agent evaluation sprint.
One AI workflow you've deployed, or are about to. We derive scoring criteria from how the work is actually judged in your organization, run a structured expert evaluation, and hand you a severity-ranked account of where it holds and where it breaks.
We'll tell you what we'd build, what we'd test, and what a fixed-scope first step looks like.