Shipping AI features that actually help users

It is easy to bolt a chat box onto a product and call it AI. It is much harder to ship something users come back to. The difference is rarely the model — it is the engineering around it.

Start from a job, not a model

We begin with a specific task a user is already doing the slow way, and ask whether a model can make it meaningfully faster. If the answer is vague, the feature is not ready.

Evals before polish

Before we tune prompts or UI, we build a small evaluation set of real inputs and expected behaviour. It turns 'feels better' into a number we can move.

Guardrails and fallbacks

Production AI needs graceful failure: timeouts, retries, and a sensible non-AI path when the model is wrong or slow. Users forgive a feature that occasionally declines to answer; they do not forgive one that confidently breaks their workflow.