VERIFIED, NOT ASSERTED
Your evals passed.
Production didn’t.
We run the autopsy on AI that scores perfectly offline and fails under real load. We reproduce the failure, prove the root cause, and hand you numbers anyone can rerun.
This tells you whether it survived production, and proves it.
Judge where you must. Verify where you can.
EM 1.000 · 187 recs
EM 1.000 · 158 recs
EM 0.000 · 36 recs
EM 0.000 · 8 recs
EM 0.000 · 6 recs
EM 0.000 · 5 recs
The work is moving from building to orchestrating. The proof has to move with it.
When AI agents do the execution, they produce more than any reviewer can read, and trust can no longer rest on someone checking every line. In that shape, governance is not a policy document. It is reproducible proof that the work held: a result anyone can re-derive from the raw data, not a claim you are asked to take on faith.
That is what we build: the verification floor under your AI, so domain expertise paired with AI is not just faster, it is provable. Verified, not asserted.
What we do
How we put that floor under your system. One discipline at every level: every number reproduces or it does not ship.
Diagnostic
A reproducible investigation of a system that passes evaluation and fails under load. We identify the architectural failures, mathematical weaknesses, and production risks conventional testing missed, across the agent, model, and data stack. You receive a report where every number traces to runnable code.
Production ML autopsy
Reproduce the failure on production-realistic inputs, quantify the eval-to-production gap by slice, isolate root cause by ablation.
Calibration and silent-failure review
Find where a system is confident and wrong: the failures an accuracy score hides and a null-check never catches.
Agent trajectory evaluation
Evaluate the whole agent path, not the per-step pass. Find the step that introduces the failure when each step looks fine.
Architecture & Engineering
Design and build of AI agent and ML systems with validation at each architectural layer, from data and model decisions through deployment and monitoring. The system you ship is engineered to perform in production at the level it performs in evaluation.
Advisory
Architecture-level guidance for AI agent and ML infrastructure decisions before capital is committed, for teams where the architectural call compounds.
Jesse Moses
Every diagnostic we ship comes from a real, public artifact you can clone and rerun, not a whiteboard. The tooling holds its own author to the same standard it holds your system.
If it’s your system that is failing
One system. We reproduce the failure, prove the root cause, and hand you a report where every number reruns. Fixed scope and a fixed window; we scope and price it on the call.
Book a Production ML Autopsy ›