Treating AI as infrastructure, not a feature

The moment a model call sits on a critical path, it becomes a dependency with latency, failure modes, and a bill. That is the same category as a database or a payment provider, and it deserves the same engineering seriousness.

Products that treat it as a headline feature tend to expose the raw model to the user and hope. Products that treat it as infrastructure wrap it, constrain it, and let the interface stay stable while the model underneath changes.

Free-form text is difficult to build on. Where a result feeds anything downstream, we ask for structure and validate it before it is allowed anywhere near application state.

Validation at the boundary means a malformed response becomes a handled error instead of a corrupted record. It also makes the failure visible in logs rather than three screens later as a mystery.

A model will eventually produce something confident and incorrect. The right design question is how much that costs the person using it.

In Trade Estimate AI, an estimate is a draft a professional reviews and edits, not a number sent to a client automatically. The human stays in the loop where the stakes justify it. That is a product decision, and it is the one that makes the AI usable rather than risky.

Per-call pricing means usage patterns and unit economics are the same conversation. A feature that calls a large model on every keystroke is a design decision about the business, whether or not anyone framed it that way.

Caching, smaller models for narrow tasks, and doing work on the device where it is possible are not just optimisations. They are what makes a feature survivable at scale.

People care that the estimate is right, the recipe uses what is in the fridge, and the check-in lands at a moment that makes sense. The technique is our problem, not theirs.

When a product's main claim is the technology it uses, that usually means the job it does has not been made clear yet.