Demos lie. Dashboards don't.
Most AI features look incredible in a demo and crumble in production. The reason is almost never the model — it's everything around it: retries, cost guards, evals, schema drift, and the silent 2% of requests that quietly poison the UX.
At Cerebrix Studio we ship AI the same way good teams ship payments: with observability baked in from commit zero.
The four loops that keep AI honest
- Eval loop — golden sets, regression suites, and a CI gate that blocks merges if accuracy drops more than 2 points on the canon set.
- Cost loop — per-request token, latency, and dollar telemetry. A budget alarm fires before the bill does.
- Trace loop — every prompt, tool call, and retrieval is captured with a correlation ID you can replay in one click.
- Feedback loop — thumbs, edits, and silent abandonments flow back into the eval set automatically.
Without these loops, "AI in production" is just a pager rotation with extra steps.
What we put in week one
- A
traceIdon every inference, propagated through your stack. - A
costGuard()wrapper that hard-caps spend per tenant and per route. - A small but real eval set (50–200 cases) covering the failure modes you've already seen.
- A rollback switch wired to a feature flag, not a deploy.
The boring outcome
Once these are in place, AI features start to behave like any other reliable service: deploys are uneventful, the on-call rotation is quiet, and the model upgrade you've been afraid of becomes a 10-minute experiment instead of a three-week project.
That's the bar. Magic that ships, then keeps shipping.
