Adding AI to your product without the hype

There has never been a wider gap between AI demos and AI products. A convincing prototype takes an afternoon; a feature people rely on every day takes real engineering. The graveyard of 'AI-powered' launches is full of features that dazzled in a pitch and quietly broke the moment real users fed them messy, real-world input.

So we don't start with the model — we start with the problem. 'Let's add AI' is not a requirement; 'users waste twenty minutes searching our docs' is. When the AI is pointed at a specific, painful task with a measurable outcome, it earns its place. When it's bolted on because the technology is fashionable, it becomes a maintenance burden that impresses no one for long.

Matching the right technique to the problem matters more than picking the biggest model. A support assistant that must cite your real documentation needs retrieval (RAG), not a model improvising from memory. Sorting tickets is classification. Drafting copy is generation. Most failed AI features are a mismatch between the problem and the approach — solved by thinking about the task before reaching for a model.

Reliability is where the serious work begins. Language models are confidently wrong sometimes, so we design for it: grounding answers in retrieved sources, constraining outputs to validated formats, and building evaluation suites that catch regressions the way unit tests catch bugs. Every AI feature has a fallback for when the model is uncertain — silence or a graceful hand-off beats a confident lie.

Cost and latency decide whether a feature is usable at scale. We cache aggressively, stream responses so the interface feels alive while the model thinks, and pick the smallest model that clears the quality bar rather than defaulting to the largest. A feature that costs more per use than it returns, or makes users wait five seconds, won't last no matter how clever it is.

The interface around the AI matters as much as the AI itself. We set expectations honestly, show the sources behind an answer, make it obvious when something was AI-generated, and always let users edit or override. Trust is earned by being transparent about what the system can and can't do — not by pretending it's magic.

And we measure whether it actually helps. An AI feature is held to the same standard as any other: does it move a real metric? The ones that earn their keep, we invest in. The ones that were only ever a demo, we have the discipline to remove. Shipping AI well is less about the model and more about the judgment around it.