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CTO, AI-native finance SaaS shipping FP&A AI into finance customers

The FP&A agent customers could trust.

~60%->95% stated FP&A accuracy (~90% measured; not an audited fact), 144% NRR, 20% fewer false positives, 90+ regression scenarios, and rollout to 100+ customers.

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The proof

The results, kept honest.

~0%->95%stated FP&A accuracy, with ~90% measured; not an audited fact
0%net revenue retention as customers expanded
0%reduction in false positives after the eval pipeline went live
0+high-risk document scenarios covered in regression testing
01

The challenge.

The CTO had a finance copilot customers liked and a number they could not stand behind. FP&A accuracy stalled near 60%, and a finance team that has to double-check every figure is not using AI; it is auditing it.

02

The approach.

  • Context layer, retrieval ranking, and prompt tuning for the FP&A workflow.

  • Reviewer-agent checks and deterministic SQL fast paths for finance-critical questions.

  • Criticality-weighted eval DAG with 90+ regression scenarios, made the deploy gate so nothing shipped until the golden set passed.

03

What shipped.

  • Eval harness deploy gate.

  • Criticality-weighted DAG.

  • Reviewer-agent checks.

  • SQL fast paths for finance-critical questions.

  • Agent-behavior visibility for customer AI reviewers.

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