How a Finance Team Eval-Checked Every AI Run

A regulated finance function layered an eval check over every AI-generated analysis, enforcing accuracy, PII-safety, and authorization before an independent model signs it off.

Every run

Gated for accuracy, PII, access

Implementation Time

Unmukt Raizada

Founder & CEO

TrustEvals

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The Challenge

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

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.

AI Role

AI generates first-draft financial analysis from permissioned P&L data via governed-context RAG and pre-vetted templates, with multi-model agentic routing and Claude as the primary model. A per-run eval trust layer enforces accuracy, PII-safety, and enterprise authorization on every generation, and an independent reviewer model runs adversarial maker-checker review, flagging inconsistencies before a human signs off.

AI Model

Claude (primary model), governed-context RAG over proprietary P&L data, multi-model agentic generation/routing, pre-vetted analysis templates, per-run eval trust layer, adversarial maker-checker reviewer model, in-document review/approval in permissioned spaces

Infrastructure

Proprietary P&L data (governed RAG source) • Pre-vetted analysis templates • Permissioned in-document review and approval spaces

Integration Points

Governed-context RAG retrieval over permissioned P&L data • Multi-model agentic generation and routing • Per-run eval trust-layer gate • Independent maker-checker reviewer handoff before sign-off

Impact

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.

Technology Utilized

Implementation Complexity

Risk & Compliance

Time Savings

Best Fit For

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.

Unmukt Raizada

Founder & CEO

TrustEvals

Founder & CEO of TrustEvals. Builds AI evaluation and governance infrastructure for finance, real estate and regulated software — eval harnesses, semantic data dictionaries, and AI audits.

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Industry:

Financial Services

Business Function:

Finance & Accounting

Legal & Compliance

Company Size:

Project Cost:

Cost not disclosed

Ownership:

Organization Type:

AI Pattern:

Knowledge Management & Search (RAG)

Data Synthesis & Reporting

Value Type:

Risk & Compliance

Time Savings

AI Model:

Frequently Asked Questions

How can a regulated finance team run AI financial analysis while enforcing accuracy and PII-safety on every output?

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.

What AI models and approach were used for the financial analysis harness?

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.

What results did the finance function achieve?

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.

How long did the financial analysis harness take to build?

The record does not specify a timeline for the engagement.

Who is an eval-checked financial analysis harness best for?

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.