Shipping AI features that survive production

Most AI features die in the gap between a working prototype and a production system. The prototype proves the model can do the thing. Production asks a different question: what happens when it's wrong, when it's slow, or when ten thousand people hit it at once.

Evaluation before deployment

We build an evaluation harness before we build the feature, a fixed set of real inputs with known-good outputs, scored automatically on every change. Without it, "the model seems better" is just a feeling, and feelings don't survive a production incident review.

Design for the wrong answer

Every model is wrong some percentage of the time. The product decision that matters isn't accuracy, it's what the interface does when the model is confidently incorrect. Confidence scores, fallback paths, and a clear way for a human to override are not nice-to-haves; they're the actual feature.

Cost is a design constraint

A feature that costs more to run than it earns doesn't ship, no matter how good the demo looked. We track cost per request from day one, alongside latency and accuracy, so the tradeoffs are visible before they become a surprise on next month's invoice.