E1
Golden sets from real work
The crawl stage produces hand-verified examples of the task done right. That becomes the test set. Evals are built from your workflow, not a public benchmark.
Evaluation / prove it before you trust it
Every system we ship passes an evaluation gate built from your real work, and keeps passing it in production: before go-live, after go-live, and at every sign-off in between.
Before go-live
E1
The crawl stage produces hand-verified examples of the task done right. That becomes the test set. Evals are built from your workflow, not a public benchmark.
E2
We test the inputs that break systems: malformed data, ambiguous instructions, prompt injection, and the corner cases the manual process taught us to expect.
E3
Where the output touches a patient, a case file, or a dollar, a domain expert reviews eval results before anything is granted autonomy. For clinical work, that reviewer is a clinician.
The sign-off
A system goes live with the least autonomy that does the job. Eval results, the failure-mode review, and the rollback plan go to an accountable owner, and autonomy expands only as the production record earns it. That is the run stage of little ai, and it is a decision a person makes, on evidence.
After go-live
M1
Production decisions are logged and replayable. When a result looks wrong, we replay the run and see exactly why, not guess from a dashboard.
M2
The eval suite keeps running against production behavior. Model updates, policy changes, and improvements the system proposes for itself do not ship until they pass the same gate the original system did.
M3
Performance is watched for drift against the golden set. Actions are built to be reversible, so a regression gets rolled back, not explained away.
We will show you the harness, the traces, and the sign-off record for a comparable system, under NDA where appropriate.
Request an eval walkthrough