Shipping LLM features without the hype

Every team wants AI features. Far fewer want the unglamorous scaffolding that makes them survive contact with real users — and that gap is exactly where most "AI products" quietly die.

We treat a model as one unreliable service among many. That means strict validation on the way in and out, graceful fallbacks when a response comes back malformed, and logging every prompt and completion so failures are debuggable rather than mysterious.

The result is boring in the best way: features that degrade instead of breaking, costs you can predict, and a system your on-call engineer can actually reason about at three in the morning.