From Ingestion to Agents: How AI Teams Build on Document Intelligence — Adit Abraham, Reducto

This talk introduces agentic OCR that performs token-level corrections (e.g., changing an 'O' to a '0') rather than full page rewrites, significantly improving accuracy and efficiency. It also details how to handle tables by converting simple ones to markdown and complex ones to HTML, and uses natural language rendering for retrieval to overcome embedding model limitations. The post discusses classification and splitting for orchestrating large documents and the use of agent harnesses with tools like code interpreters and self-checks to solve complex problems (e.g., converting line charts to data tables) that frontier models struggle with, addressing precision and recall trade-offs. It stresses the importance of evaluations at every stage and envisions agents interacting with file systems and CLIs.