Building an AI demo is easy; building a system that holds up in production under load is hard. The difference isn't the model — it's the process.
The real problem: reliability, not the model
In projects like credential evaluation, the real challenge was that results had to be reliable, auditable and repeatable. One correct output in a demo isn't enough; the system has to be right thousands of times.
The solution: an eval-re-eval loop
Instead of relying on one model, we put the process in a loop: extract, propose, check against a goldset, and a human sign-off — every step with an audit trail. This lets us swap models mid-project, because we measure quality rather than guess it.
The result? A process that cut six days of manual work to eleven minutes and runs reliably, every day.