No eval, no ship.
A demo proves nothing. Before a model reaches production we hold it to a labeled benchmark and track it in CI — for quality, safety, fairness, and cost — so every release is measurably better than the last.
Six dimensions, every release.
Task success
Did it actually do the job? End-to-end success on real tasks, not a proxy score.
Factuality & citations
Is the answer true and traceable? Grounding and citation coverage scored per release.
Safety & refusal
The right refusals under adversarial and jailbreak pressure, tested continuously.
Bias & fairness
Outcome parity on consequential decisions — especially lending and eligibility.
Robustness
Holds up on paraphrases, edge cases, and the messy inputs of the real world.
Cost & latency
Per-task cost and p95 latency tracked and routed, so quality fits the budget.
From labeled set to CI gate.
Run against a labeled, versioned test set and compare to the incumbent baseline.
Scale qualitative scoring with a judge model, calibrated against human ratings.
Domain experts score the hard, high-stakes cases the automation can't settle.
Adversarial probing for jailbreaks, leakage, and unsafe behavior before release.
Every change re-runs the suite — no eval, no merge, no ship.
Public leaderboards rarely match your task. We build private, versioned benchmarks from your real cases, keep a held-out split the model never trains on, and report the delta against your current baseline — honestly, including where it regressed.
Bring us a hypothesis. Leave with a system.
Tell us what's eating your team's time. We'll give you an honest read on whether AI is the right tool — and if it is, a scoped v1 with a timeline and cost.