The Missing Infrastructure for AI Agents: Documents | Reducto’s Adit Abraham

AI agents struggle with real-world work because critical information remains trapped in complex, messy documents like PDFs and spreadsheets. Traditional OCR technology fails to handle structural intricacies, leading to silent data loss that carries significant consequences in high-stakes fields like healthcare, finance, and legal services. Reducto addresses these challenges by utilizing specialized visual language models and self-correcting harnesses that ensure high-fidelity extraction and determinism. By moving beyond simple parsing to include context-aware reasoning and document editing, these tools enable agents to automate complex, document-heavy workflows that were previously manual. With over seven billion pages processed, the focus shifts toward achieving near-perfect accuracy and scalability, allowing AI to reliably handle tasks ranging from medical claim adjudication to tax filings, ultimately reducing the inefficiency of human-in-the-loop processes.