Document Insights

Enterprise teams work through huge volumes of complex documents, contracts, energy and grid forms, filings, where the important data is buried in inconsistent layouts. Document Insights is an AI product that reads these documents and extracts the relevant data points automatically.

But automation alone isn’t the point. The value of an extraction product depends entirely on whether people can trust the results, verify them quickly, and stay in control of what the AI decides. That trust problem was the real design challenge.

Extraction models are confident, fast and occasionally wrong, and in an enterprise context, a wrong value can be expensive. The interface had to make the AI’s certainty legible, let users correct and confirm values without friction, and never hide what the model was doing. The goal was to turn a black box into a reviewable workflow.

The work shaped how users move from raw documents to confirmed, structured data, making the AI’s confidence legible, keeping every value traceable, and letting teams scale automation only as far as their trust in the model allows.

Better model accuracy doesn’t automatically create confidence. People trust AI when they can see how sure it is, check where a value came from, and correct it without fighting the interface. Legible confidence and easy verification did more for adoption than raw accuracy alone.

This project sharpened how I design AI-native products: surface uncertainty honestly, keep humans in control, and design the review experience as carefully as the automation itself.