Your AI demo works.
Production is where it breaks.
A demo proves the model did the thing once. A production feature does it ten thousand times, for inputs you didn't anticipate, on the days the API is down, without quietly running up the bill. We build the engineering around the model that makes it hold up.
The four things production breaks on.
A demo proves the model returned a good answer for one input on one day. It doesn't prove the things that decide whether the feature survives its first month.
The model is sometimes wrong
Modern models are confident regardless of accuracy. They invent facts, misread instructions, and occasionally produce output the brand should never ship. The question isn't whether this happens. It's how often, how visible it is, and what happens when one slips through.
The API is sometimes down
Every provider has incidents, rate limits, and regional outages. A call to an inference endpoint is no more reliable than any other network call, and treating it as a transparent function is how a demo becomes an outage.
The input is sometimes weird
Users paste in forty pages of unrelated text, write in a language the prompt wasn't designed for, or send something that reads as adversarial. Real inputs are a much larger space than the inputs a demo shows.
The bill is sometimes huge
A feature that costs four cents a call costs four thousand dollars a day the moment it goes viral. Without per-feature cost tracking, the first warning is the monthly invoice.
The boring part is the work.
Production-ready is the list of things a demo never had to do. None of it is exotic. It's the infrastructure that turns a model call into a feature.
Evals, not vibes
A test set of real or representative inputs with quality criteria, graded automatically. When the prompt, model, or settings change, the evals tell you whether quality went up or down, instead of waiting for a customer to notice.
Retries with a fallback path
The model call sits behind retries with backoff and a defined fallback when the model is unavailable: a smaller model, a cached answer, or a graceful message. Never a silently broken feature.
Observability you can use
Every call logged with input, output, latency, tokens, and cost, with sampling so a human can review a representative slice. When behavior shifts because a provider changed something underneath you, you see it before customers do.
Cost monitoring at the feature level
Spend tracked per feature and per user, alerts when daily spend crosses a threshold, and a hard cap so a runaway loop or an abusive user can't drain the budget overnight.
Guardrails on input and output
Inputs constrained where the model is sensitive (prompt injection, off-topic or sensitive queries). Outputs validated against the structure the rest of the system expects, so a malformed response doesn't break the next step.
A way to know when it's wrong
Explicit feedback, downstream signals (the user re-asked, didn't click the suggestion, transferred to a human), or periodic sampling against the eval set. Without one of these, quality drifts and nobody notices.
The fix is rarely a better prompt.
The streaming AI code generator we built for a global user base ships with the full stack: evals on every prompt change, multi-provider fallback, per-language quality monitoring, token-level cost tracking, and a feedback signal that catches regressions before users do.
A separate client came to us with an assistant a previous team had shipped. The model was fine. What was missing was the scaffolding: no evals, no logging, no cost dashboard, no retries. The fix wasn't a better prompt. It was the engineering around it.
From AI demo to production: what the demo doesn't proveFive questions to ask any AI vendor.
These test whether a vendor builds for production. The shape of the answer matters more than the specifics.
How will we know if the model is getting worse?
Looking for: Evals, sampled review, a feedback signal, regression alerts.
The wrong answer: “We'll keep an eye on it.”
What happens if the provider has an outage?
Looking for: Retries, a fallback path, graceful degradation.
The wrong answer: “Their uptime is good.”
How are we tracking cost per feature and per user?
Looking for: Per-call logging, alerts, a hard cap.
The wrong answer: “We'll watch the monthly bill.”
What test set runs before you change the prompt or model?
Looking for: A real eval set with grading, run automatically.
The wrong answer: “We test it manually.”
What happens on a malicious or off-topic input?
Looking for: Input validation, system-prompt isolation, output checks.
The wrong answer: “The model handles that.”
A team that answers all five with grounded specifics builds AI that survives its first month. A team that stays vague on more than one or two is selling a demo.
See how we build AI integrationsFrequently Asked Questions
Our AI feature worked in testing but breaks in production. Why?
Can you make an existing AI feature production-ready, or only build new ones?
Does all of this slow the project down?
Do we need our own infrastructure for this?
What if we want to switch AI providers later?
Get your AI feature production-ready.
Have an AI feature you're trying to harden, or one you're about to commission? Describe it and we respond within one business day with the gaps we see and a rough scope for closing them.
Get in touch