Legal Discovery Needs Evidence Links, Not Answers

The legal use case shows why document AI should return passages, page links, provenance, and cross-references instead of unsupported summaries.

Proof / Video + alldata.md / 8:27

Audience

Legal AI teams, evals leads, enterprise reviewers

Core idea

The output is useful when a reviewer can inspect the underlying page, paragraph, document, and transformation chain.

Founder source

Legal Corpus

Watch on YouTube· 8:27

Frontier teams building legal or regulated workflows need this same discipline for eval artifacts and model-generated claims.

The videos are raw build context. These notes translate them into the shortest useful frame for creators, companies, and AI lab readers.

A correct-looking answer is not enough.

The source trail must survive chunking and embedding.

A useful legal AI narrows review, not replaces it.

Related notes stay inside the same problem area first, then move to the next useful context.

Watch + read / 12:19

A document pipeline should extract text, images, metadata, entities, relationships, and citations back to source files.

Watch + read / 5:02

The operating posture behind Teleox: treat AI output as unverified until a separate process can trace evidence and failure modes.

Watch + read / 5:31

AI-assisted engineering only scales when the workflow is built around verification, state checks, and zero-trust development.

Send the audience, data type, target task, proof bar, and sharing limits.