Case Studies

The number, the mechanism, and what the team could defend.

Three proof lanes sit together: FP&A reliability, CRE value capture, and the PE-portco audit/control pattern. Each opens a distinct proof record with the number, mechanism, and evidence label kept attached.

Proof lanes

Each lane opens the full record behind the number.

Reliability, value capture, and control are separated so the proof does not collapse into one repeated case card.

Evidence trail

The number only matters when the work beside it is visible.

Each proof artifact now shows what changed, what TrustEvals installed, what evidence was captured, and where the reader can inspect the case.

Evidence cases
AI-native finance SaaS

A release gate the product team and customers could inspect.

95%stated accuracy after the deploy-gate work
Before

~60% FP&A accuracy and repeated double-checking before release.

01Golden set
02Regression DAG
03Reviewer checks
04Release decision
Result

95% stated accuracy, about 90% measured, with 144% NRR provenance kept beside the claim.

  • 90+ scenarios
  • deterministic SQL fast paths
  • reviewer-agent checks
  • claim labels kept explicit
Open evidence
Trustable, reliable AI in production

Start with the AI work that moves the number. Keep the proof built in.

Start with Strategy, Transformation, or Fluency; use Quick Audit when the first need is an independent read on what is already running.