Financial AI Evaluation Sprint

When to call

Use this sprint when an AI workflow drafts research, interprets financial tables, summarizes market material or supports internal decisions, and the team needs a repeatable account of what it can safely do.

Who and what to bring

Product, research, risk and engineering leads provide representative prompts, approved sources, expected-answer rules, known failures, model and tool configuration, and the human decision that follows the output.

What gets tested

• Numerical accuracy, source grounding and citation fidelity
• Temporal correctness and interpretation of tables and instruments
• Omissions, edge cases and repeated-run consistency
• Prompt or model regressions and source drift
• Human-review gates for consequential outputs

How the work runs

The evaluation turns representative risks into a small test set, classifies observed failures and defines which checks can be automated. Deliverables are a test inventory, failure taxonomy, regression-suite outline and an evidence-based recommendation.

What is excluded

The sprint does not certify a model, promise regulatory compliance or replace qualified human review. It makes the automation boundary explicit instead of hiding it behind plausible prose.