60% to 95% FP&A accuracy, 20% fewer false positives, 90+ regression scenarios, and rollout to 100+ customers.
The gap was engineering, not evidence alone: context construction, RAG relevance, prompt tuning, reviewer-agent coverage, SQL fast paths, and criticality-weighted eval loops.
The reliability gap lived in context construction, retrieval relevance, prompts, review, and deterministic finance paths.
We went from being unsure of our accuracy to rolling out the product to over 100 customers and having visibility across the behavior of our agentic application. The robust implementation was further validated by our customers' AI teams, and the vendor visibility allowed us to build enterprise trust.
The work paired product engineering with evals so each failure mode had an owner and a release check.
Accuracy moved after context-layer work, RAG relevance tuning, prompt tuning, reviewer agents, and SQL fast paths.
Reduction observed after critical outputs moved through reviewer checks and release evals.
High-risk FP&A behaviors covered in a criticality-weighted eval loop.
The product moved from uncertain accuracy into rollout with visibility across agent behavior.
The hardest FP&A workflow was capped by weak context construction, not model choice alone.
The RAG path often found plausible evidence before the most relevant finance record.
Critical outputs needed reviewer agents, SQL fast paths, and higher eval weight than routine answers.
The shift was from a promising FP&A agent to a system that could be trusted across critical outputs.
Criticality decided which eval loops mattered most, then reinforced the product paths that carried the highest risk.
The same system improved agent behavior, release confidence, and buyer-facing reliability claims.
This is how AI Engineering and Evals turn production agents into reliable finance workflows.
See AI Engineering →Move from uncertain accuracy to reliable agents, customer rollout, and enterprise-ready visibility.
