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The AI said so is not a legal defense. In private markets, trusting a model's memory is a regulatory risk.

Most people think LLMs are like calculators that just need the right formula. What actually happens is the model acts like a student taking a test from memory, which leads to hallucinations.

To fix this, we move to grounded AI. This is like an open-book exam where the model is restricted to a verified textbook.

When you integrate premium data like PitchBook via Samaya AI, you create an audit trail. This means every data point in a final report is linked back to a specific API call and timestamp.

This mechanism turns a manual validation process—sifting through PDFs and Excel sheets—into a simple checkpoint. It shifts the analyst's role from hunting for the source to verifying the synthesis.

I condensed the setup for these auditable pipelines into a 12-page field guide.

The full breakdown, including the provenance chain diagram, is in the guide below.

How are you currently handling the validation of AI-generated figures in your reporting?

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