Each scenario below walks through a problem, the stack, the approach, and the outcome pattern we've seen when it's fixed — the same failure modes described in What we've seen break, in more depth.
No retrieval eval layer meant bad chunks went straight to the model, which hallucinated on top of them. Here's the fix, step by step.
No output schema enforcement meant ambiguous prompts led to the wrong tool call — automatically, repeatedly. Here's how we scoped it back.
A vision model that hit 94% in testing dropped to 61% on the actual production line. Here's what the gap was made of, and how we closed it.
A real client engagement (name withheld under NDA) — model routing, context trimming, and caching cut per-document inference cost roughly 70%.
Book a 30-minute call — we'll tell you honestly whether your situation looks like one of these, and what it would take to fix it.