Reducto: Document Ingestion for LLMs

This points to Reducto’s clearest startup wedge, becoming the outsourced ingestion team for AI apps that need clean JSON from messy files but cannot afford to build document infrastructure themselves. For most LLM startups, the hard part is not calling a model, it is turning PDFs, scans, spreadsheets, and slide decks into structured fields, table rows, and chunked sections that a model can reliably search, cite, or act on.

• Building this in house is more than OCR. A production pipeline has to detect layout, preserve tables, split long packets into logical sections, map outputs into a schema, and catch edge cases like handwriting, merged cells, and blank form fields. Reducto packages that workflow behind upload, parse, extract, split, and edit APIs.
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• The buyer is often an AI startup whose core product is elsewhere, legal copilots, insurance agents, accounting tools, research assistants. Like WorkOS for identity or Stripe for payments, document ingestion becomes a plumbing layer they would rather buy than staff up to build. That expands Reducto beyond regulated enterprises into the broader AI application stack.
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• Competition splits three ways. Cloud vendors like Google and Microsoft bundle document AI into larger platforms. Enterprise suites like Instabase sell broader workflow automation. API players like Nanonets target extraction directly. Reducto’s pitch is faster implementation for developers who need general purpose, LLM ready outputs without committing to a full enterprise suite or cloud stack.
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Going forward, the biggest upside is that every new AI app that touches documents can become a usage based customer from day one, then grow as its own end users upload more files and add more workflows. If Reducto keeps owning the first mile from raw file to structured data, it can become a default infrastructure layer inside the next wave of AI software.