The gap between an impressive demo and a reliable AI feature is enormous. Here’s how we close it.
It has never been easier to build an impressive AI demo, and never harder to ship one that holds up. The gap between the two is where most AI projects die.
A demo needs one good answer. A production feature needs a good answer every time, including for the user who types something you never anticipated, in a language you didn’t test, at ten times yesterday’s traffic. The model is maybe a fifth of the work. The rest is everything around it.
We start every AI feature with an evaluation set, before writing the feature itself. A few hundred real examples with known-good answers turn “it feels better” into a number, and every prompt change, model upgrade, or retrieval tweak runs against them. Without evals you aren’t iterating, you’re guessing.
When a feature answers questions from your own data, we build retrieval with citations. An answer that links to its sources can be checked; a confident hallucination cannot. And when retrieval finds nothing, the honest response is “I don’t know”. We design for that path on purpose instead of papering over it.
Guardrails matter as much as capability: checks on what goes into the model, checks on what comes out, cost and latency limits enforced in code, and a fallback for the day the provider has a bad one. An AI feature without a timeout is an outage waiting for a trigger.
We keep a person in the loop wherever a wrong answer is expensive. AI drafts, a person approves. That pattern delivers value months earlier than waiting for the model to be perfect, because it doesn’t have to be.
Just as often, the right call is not to use AI at all. If a set of rules, a well-shaped query, or a plain form solves the problem, the deterministic version is faster, cheaper, and easier to trust. A model in a product has to earn its complexity.
The result is AI that earns its place in a product: measured, bounded, observable, and honest about uncertainty. That’s the bar we hold our own products to.
