AI features are easy to demo and hard to ship. The gap between an impressive prototype and something users rely on is filled with unglamorous work: evaluation, guardrails, and honest UX about what the system can and can't do.
We start by finding the place where AI genuinely removes friction — not the place where it looks most futuristic. A good AI feature is often invisible: a smarter default, a faster search, a draft that saves someone ten minutes.
Then we build the evaluation harness before we build the feature. If we can't measure whether an answer is good, we can't ship it responsibly.
Finally, we design for the model being wrong. Clear affordances to correct, undo, and verify turn an occasionally-wrong assistant into a genuinely useful one.
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