From Ingestion to Agents: How AI Teams Build on Document Intelligence — Adit Abraham, Reducto
Source
AI Engineer
Author
AI Engineer
Date
Key takeaways · AI-distilled
Adit Abraham of Reducto argues that agents raise the cost of bad parsing: in RAGRetrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.Full definition → a bad parse cost one answer, but in an AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → the error compounds across every later step.
Reducto's agentic OCR applies token-level corrections, such as a zero for an O, instead of having a VLM rewrite the page, because a VLM asked to rewrite OCR may helpfully 'fix' a total the human got wrong.
Simple tables go to markdown and complex ones to HTML, but Reducto also renders tables in natural language for retrieval, since embeddingA list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.Full definition → models struggle to match a question like 'how did revenue change' to a blob of tags.
Per the talk, feeding parsed structure instead of raw PDFs lifted other frontier models past the newest one on a PDF-decision benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → and cut reasoning tokens.
He says frontier models on their own tend to be precise but silently drop rows, while document services show the opposite tradeoff; agent harnesses with a code interpreter and repeated self-checks are pitched as getting both.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
RAG — Retrieval-augmented generation — fetching relevant documents first and pasting them into the model's context so it answers from your data instead of memory.
embedding — A list of numbers representing a piece of text's meaning, so that similar meanings end up numerically close and can be searched.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
Explains specific, tested techniques for feeding agents reliable document data, where bad parses now compound across every agent step instead of costing one RAG answer, directly useful for anyone building document-processing agents.
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