Healthcare has always run on a simple loop: something goes wrong, a person notices, and only then does treatment begin. This is AI preventive healthcare in practice, and it depends on AI health risk scoring models that turn raw physiological data into something a person can actually act on.
For the past three years, "AI strategy" mostly meant "which API do we call." That's changing. A growing number of product and engineering teams are pulling specific AI workloads out of the cloud and running them directly on the user's device.
Across 1.2 million plans, we watched users trust the AI enough to book and watched them quietly abandon good itineraries for reasons that had nothing to do with itinerary quality. This article is about what separated the two.
We built a booking platform for a client operating in two markets. The core logic, search, availability, and reservations, worked fine. Launch in market one went smoothly. Then came market two, and things started breaking in places we hadn't looked.
Previously, we shared how to integrate a local LLM into a Flutter app. Today, we will add the RAG to the equation.
Claude Opus 4.6 scored 80.84% on SWE-bench Verified in February 2026: the benchmark that measures whether a model can actually fix real GitHub issues. That number matters because it's the point where repo-wide refactoring stops being a party trick and starts being something you can ship with.
