AI Reliability
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.
What the Demo Doesn't Prove
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.
Confidence and accuracy are separate things in a language model, so a bad answer arrives looking exactly like a good one. What decides the business risk is the rate, who encounters it first, and whether anything catches it ahead of a customer.
Model providers have incidents, rate limits, and regional outages like any other dependency you take on. With no fallback path in the design, your product's availability is capped by someone else's status page.
Demo inputs get chosen. Live ones include pasted documents, languages nobody scoped for, and the occasional deliberate probe. Coverage across that whole range is what separates a shipped feature from a convincing rehearsal.
Usage pricing means cost scales with success. Four cents a call stays trivial until volume turns it into four figures a day, and with no per-feature tracking the invoice becomes the alerting system.
What Production-Ready Means
Production-ready is the list of things a demo never had to do, and none of it is exotic engineering. These six are what stands between a model that answers and a feature you can sell.
Representative inputs with grading criteria, scored automatically on every change. Swap the prompt, the model, or a setting and you get a number back, rather than waiting for a support ticket to tell you it got worse.
How AI evals actually workThe 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.
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.
How to tell when your AI quietly brokeSpend attributed to each feature and each user, thresholds that page someone when a day runs hot, and a ceiling that stops a runaway loop before it empties the account.
What AI costs at volume, and how to control itConstraints on the inputs a model reacts badly to, covering prompt injection and off-topic or sensitive queries. On the way out, responses are checked against the shape downstream code expects, so a bad one fails safely instead of propagating.
Keeping a model on-task when users go off-scriptExplicit thumbs, behavioural tells (a repeated question, an ignored suggestion, a handover to a person), or scheduled sampling scored against the grading set. Lacking all three, drift goes unmeasured until it is large.
In Practice
Our streaming code generator serves a global user base, and every one of the six elements above is in it: graded evals gating prompt changes, failover across providers, quality tracked language by language, cost measured at the token, and a feedback loop that surfaces regressions before customers report them.
Another client arrived with an assistant a previous team had shipped. Both the model and the prompt were sound. Everything around them was absent: no evals, no logging past raw responses, no cost visibility, no retries. The work that made it production-ready was all engineering around the model.
Before You Sign
These test whether a vendor builds for production. The shape of the answer matters more than the specifics.
Looking for: A graded set that runs on a schedule, sampled human review, alerts when scores move.
The wrong answer: “We'll keep an eye on it.”
Looking for: Retries, a second provider to fail over to, a degraded mode that still answers.
The wrong answer: “Their uptime is good.”
Looking for: Logging on every call, a per-feature breakdown, spend alerts, a hard ceiling.
The wrong answer: “We'll watch the monthly bill.”
Looking for: A graded eval set wired into the deploy, with results that can block it.
The wrong answer: “We test it by hand.”
Looking for: Constraints on input, an isolated system prompt, structural checks on what comes back.
The wrong answer: “The model handles that.”
Grounded specifics on all five point to a team that has run AI in production before. Hedging on more than one or two points to a demo with a sales deck attached.
See how we build AI integrationsFAQ
The model is usually fine. What's typically missing is the scaffolding around it: evals, retries, observability, cost caps, and guardrails. That gap is the most common reason a feature that demoed well gets quietly switched off later.
Both. A common engagement is taking an assistant a previous team shipped and adding the evals, logging, fallbacks, and cost monitoring it never had.
It's normal software engineering applied to a new component, so it runs alongside the build rather than bolting on at the end. Retrofitting it later costs more, which is the case for doing it from the start.
No. Observability, cost tracking, and fallbacks run with standard tooling and your existing stack. For sensitive data we can scope deployment within your infrastructure.
The new ones mostly exempt a support assistant: the laws that start in Colorado, Washington, Connecticut, and Rhode Island on January 1, 2027 each leave out, in some form, a bot built for customer support. Older rules in Maine, Utah, and California already require any business chatbot not to pass as a person, and the EU asks whoever built one to say it is an AI. Both come down to behaviour that should be built into the chat window and tested before launch.
What the new AI chatbot laws ask of your websiteWe build model-agnostic, so the retry, fallback, and eval layers aren't tied to one provider. Swapping the underlying model is a configuration change, not a rewrite.
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.