Every "add AI to your product" tutorial assumes you are starting fresh. Greenfield repo, clean data, no users yet. Real integration work looks nothing like that.
Last year our team picked up a fintech client with a loan-application platform that had been running since 2014. Node.js backend, a Postgres database that three different teams had touched, and a checkout flow that processed real money every few seconds. The ask sounded simple: use an LLM to pre-screen loan applications and flag the risky ones for a human.
It was not simple. Here is what broke, in the order it broke, and the pattern that finally held.
Break #1: The Synchronous Call That Took Down Checkout
The first version was the obvious one. A developer added the LLM call directly into the application-submission handler. Application comes in, call the model, get a risk score, continue.
