- A demo proves an idea works once. Production means it has to keep working on traffic it's never seen.
- Eval suites that run in CI catch quality regressions before a deploy — treating AI quality like any other engineering discipline.
- Cost and latency need dashboards from day one, not a surprise on next month's invoice.
- Guardrails and confidence routing send the hard cases to a human instead of letting the model guess.
- An abstraction layer over the model keeps you free to swap providers as pricing and quality shift.
The demo is the easy 20%
Anyone can wire up an impressive demo in an afternoon. The gap between that and something you’d put in front of a customer is the boring 80%: evals, monitoring, cost control, failure modes and governance.
The demo works because the happy path is the only path anyone tested. Production traffic finds every edge case the demo never saw — malformed input, a slow upstream API, a user who phrases the same request four different ways.
Evals or it’s vibes
Without a deterministic eval suite, “is it working?” is just a feeling. We build eval sets that run in CI so a quality regression blocks the deploy — the same discipline you’d expect for any other production code.
We treat an eval suite the same way a backend team treats unit tests: a gate, not a nice-to-have. If a prompt or model change drops accuracy on the eval set, the deploy blocks automatically, before it ever reaches a user.
Observability from day one
Latency, cost and quality need dashboards, not guesswork. When a model drifts or a prompt change spikes cost, you should see it immediately — not in next month’s invoice.
Cost dashboards matter as much as quality ones. A single unbounded retry loop or an oversized context window can turn a reasonable per-request cost into a five-figure surprise by the end of the month.
Guardrails and hand-offs
Retrieval grounding, structured outputs, confidence routing and human-in-the-loop on risky paths. We treat hallucinations as an engineering problem, not magic — and we route the hard cases to people.
Confidence routing means the model handles what it's actually good at and hands off the rest. A support bot that's 60% confident shouldn't guess — it should escalate, with the full context already attached.
No vendor lock-in
An abstraction layer over the model means you can swap Claude, OpenAI or open-source without rewriting product code. Pick by use-case, not by allegiance.
Swapping providers isn't just a cost play. Model quality shifts, new releases land, and a client's compliance requirements can rule a provider in or out overnight. The abstraction layer is what makes that a config change instead of a rewrite.
Security and infra are part of the boring 80% too
The unglamorous work doesn't stop at evals and observability. Access control, encryption and infrastructure monitoring are just as much a part of shipping AI safely into production — and just as often skipped by teams chasing the demo. We treat security review as a standard step in any AI integration project now, not an optional add-on.
That means every AI integration we scope now includes an access-control and monitoring review as a standard line item, not a follow-up project six months after launch when someone finally asks who can see what.