Building FP&A AI customers could trust.

~60%->95% stated FP&A accuracy (~90% measured; not an audited fact), 144% net revenue retention reported during the same period, 20% fewer false positives, 90+ regression scenarios, and rollout to 100+ customers.

The buyer’s exposure was simple: a finance team will not act on a number it does not trust. Tensile AI moved the system by changing the mechanism.

Start with the architecture.

The reliability gap lived in context construction, retrieval relevance, prompts, review, and deterministic finance paths, more than in model choice.

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. Customer AI teams also reviewed the implementation, and the vendor visibility allowed us to build enterprise trust.

CTO, AI-native finance SaaS

Engineer the reliability loop.

Tensile AI authored the implementation as harness engineering: deciding where deterministic SQL belonged, where an LLM helped, and where reviewer agents had to check the work before release.

• Context-layer redesign

• RAG relevance tuning

• Prompt and fallback paths

• SQL fast-path scope

• Failure-mode taxonomy

• Reviewer-agent checks

• Criticality-weighted eval DAG

• Regression reinforcement

Ship the deploy gate.

The eval harness became the release gate. High-risk FP&A behaviors were weighted more heavily than routine answers, and each failure mode had an owner before the product went back to customers.

What shipped.

• 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, run as the deploy gate.

Proof.

• ~60%->95% stated FP&A accuracy, with ~90% measured; not an audited fact.

• 144% net revenue retention alongside the reliability work, as customers expanded.

• 20% reduction in false positives after the eval pipeline went live.

• 90+ high-risk document scenarios covered in regression testing.

• Rolled out to 100+ customers with agent-behavior visibility.

What the team can now defend.

The product team can show the number, the mechanism, and the pipeline that keeps it true: eval harness, deploy gate, criticality-weighted DAG, reviewer checks, SQL fast paths, and customer-visible agent behavior.

Next move: Build (deploy gate) -> Run (continuous reads on every release).