The model is usually the smallest part of shipping an AI-powered web application. The harder, less-discussed work is everything around it: getting the right data to the model, serving predictions fast enough to be useful, and presenting the output in an interface people actually understand.
The inference layer
A trained or third-party model needs to be reachable by the application, which means an inference API — typically built in FastAPI or as part of the Laravel backend — that accepts input, runs the model, and returns a structured result. This is a normal backend service from the application's point of view; the fact that it happens to call a model is an implementation detail.
The data pipeline
Whatever the application collects — movement data, images, user activity — needs to reach the model in the format it expects, consistently. For a project like an AI rehabilitation platform, that means turning raw motion capture into a structured representation the model can process reliably, every time, not just in a demo.
The interface
AI output is only useful if someone can act on it. That usually means a visualization, a recommendation surfaced at the right moment, or a structured result embedded into an existing dashboard — not a raw JSON blob. Design work for AI features focuses on making the model's output legible and actionable inside a normal user flow.
Where it fits in the architecture
In most builds, the web application (often Laravel and React) handles authentication, business logic, and the interface, while the inference layer sits behind an internal API the application calls when it needs a prediction. This keeps the model swappable — a better version can be deployed without touching the rest of the application — and keeps the application itself simple.
What tends to get underestimated
Latency and failure handling. A model call is a network request like any other: it can be slow, and it can fail. Production AI features need the same loading states, timeouts, and graceful fallbacks as any other asynchronous part of an application — treating the AI call as "special" and skipping that handling is a common source of production issues.