AI-native product engineering

Most AI features fail in the same place. The demo works, and then the model returns prose where the application needed a value. We build features that constrain the model rather than hope for it: forced structured output and tool use, so a result arrives as a typed object the rest of the system can rely on.

Answers are grounded in the client's own material through retrieval, not left to the model's recall. Where the work happens away from a network, inference runs on the device. Untrusted input reaching a model stays untrusted, so an instruction buried in a document cannot redirect what the feature does.

Every prompt is evaluated against a fixed set of cases before release, and every call is logged after it. A change in model behaviour then shows up as a failing test rather than a support ticket — which is the difference between an AI feature you can ship and one you can keep.