How a Finance Team Eval-Checked Every AI Run
Every run
Not disclosed
Not disclosed
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.
TrustEvals built a complete financial-analysis system combining 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 reviewer model. Review and approval happen in-document within permissioned spaces.
Three layers work together: governed-context RAG with agentic generation over pre-vetted templates; an eval trust-layer that enforces accuracy, PII-safety, and enterprise authorization on every single run; and a maker-checker adversarial review where an independent model flags inconsistencies before sign-off.
Best fit for regulated finance teams that want faster AI-generated analysis but cannot accept any run that is inaccurate, leaks PII, or exceeds a user's authorization.
Enforced on every run
Adversarial maker-checker review
Governed-context RAG
Unmukt Raizada
The finance function worked with the experts to build a governed-context RAG system with multi-model agentic generation over pre-vetted analysis templates. A per-run eval trust layer and an adversarial maker-checker reviewer mean accuracy, PII-safety, and enterprise authorization are enforced on every single run before sign-off.
It combines knowledge-management RAG and data synthesis: governed-context retrieval 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 independent reviewer model for adversarial maker-checker review.
The eval trust layer checks accuracy, PII-safety, and authorization on every generation, an independent model flags inconsistencies before any analysis is signed off, and generation draws only on permissioned P&L data feeding pre-vetted templates.
The record does not specify a timeline for the engagement.
It is best suited to regulated finance teams that want faster AI-generated analysis but cannot accept any run that is inaccurate, leaks PII, or exceeds a user's authorization.
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