How to build a secure, observable, and vendor-agnostic platform between enterprise applications and large language models
The first AI feature we shipped took less than a week. A product team identified a use case, picked a large language model, added an API call to the backend, and returned the response to the user.
Six months later, the picture looked very different. Five separate teams were integrating with AI, each with its own model provider, its own retry logic, and its own copy of a prompt template that had quietly drifted from the others. Nobody could say with confidence why one application's monthly bill was three times another's for what looked like similar workloads.
We didn't have an LLM problem. We had a platform problem.
That gap — between "call a model" and "run AI reliably across an organization" — is what this article is about.
