TrustEvals started from the data, not the model. They built a semantic data dictionary mapping the customer's financial data — and validating it surfaced gaps that were fine for a human analyst but left a language model without enough context. With the map in place, a router classified each incoming question and answered routine ones through templated fast paths built for that question type. The hardest asks — forecasts and what-if scenarios — were sent to a separate, purpose-built predictive engine rather than trusting the LLM with math. Wrapping everything was a testing system that scored every answer against custom evals on held-out data, ran security checks, and compared OpenAI, Claude and Gemini head-to-head to pick the best model per job. The hard rule: nothing reaches a customer until it passes. Human-in-the-loop flows let the team update the eval set, catch poor answers and remediate immediately. The lesson they carried forward: start evals on day one and validate the data dictionary before locking architecture. Delivered in 4–8 weeks.