Katot AI

Katot AI started from a simple product constraint: useful extraction systems need to produce structured output that can survive contact with real workflows. The work focused less on generic chat interactions and more on turning documents into dependable fields, entities, and schema-bound results.

The product direction centered on tasks like invoice parsing, contract analysis, and custom extraction pipelines where the downstream consumer is another system, not just a person reading an answer. That shaped both the interface and the model behavior toward reviewability, predictable structure, and iteration around edge cases.

What made it interesting was the practical layer around the model itself: schema design, evaluation on messy inputs, and deciding where to make the user explicit about expectations instead of hiding everything behind a single prompt box.