Case study · From the Pluris libraryReceipt no. 5713
What sucked
The function needed to accelerate analyst output while maintaining strict governance: every output had to be accurate, free of PII leakage, and authorized for the person requesting it.
Ungoverned AI generation posed regulatory liability.
What they built
Tensile built a governed financial-analysis system for a regulated enterprise finance function: governed-context RAG over the firm's P&L data feeding pre-vetted templates, multi-model agentic generation with Claude as the primary model, a per-run eval trust layer, and an adversarial maker-checker reviewer, with review and approval handled in-document in permissioned spaces.
What changed
Enforced on every run
The eval trust layer checks accuracy, PII-safety, and authorization on every single generation.
Adversarial maker-checker review
An independent model flags inconsistencies before any analysis is signed off.
Governed-context RAG
Generation draws only on permissioned P&L data feeding pre-vetted analysis templates.
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