Reducto

Reducto is an "agentic document platform" (YC W24) that turns messy, unstructured documents, PDFs, scans, spreadsheets, faxes, handwriting, into clean, LLM-ready output through a vision-first parsing pipeline. Rather than positioning itself as a single OCR endpoint, it bundles Parse, Extract, Split/Classify, and Edit APIs plus a "Studio" UI for building and evaluating document pipelines. Its core differentiator is accuracy on hard layouts: it pairs custom vision models with traditional OCR and bounding-box attribution so every extracted value can be traced back to a region of the source page, which matters for regulated finance, healthcare, insurance, and legal workloads. The company is well-capitalized and has real enterprise traction, it raised a $75M Series B led by a16z in October 2025 (bringing total funding to ~$108M), reports processing over a billion pages, and names Scale AI, Harvey, Vanta, Rogo, and JLL as customers.

Where Reducto wins is complex table and form extraction and at-scale production reliability. On its own open-source RD-TableBench (1,000 PhD-annotated complex tables), Reducto reports a 90.2% average table-similarity score, ahead of Azure Document Intelligence (82.7%), AWS Textract (80.9%), Claude 3.5 Sonnet (80.7%), GPT-4o (76.0%), and Google Cloud Document AI (64.6%). That benchmark is self-published, so it should be read with some skepticism, a newer entrant (DocLD) claims to beat it at 92.4% on the same set, but the dataset and scoring code are open and reproducible on Hugging Face, which is more transparency than most competitors offer. The pricing shift to a pay-as-you-go Standard tier (free up to 15,000 credits, then $0.015/credit-page, no page caps) in late 2025 made it far more accessible to startups than its older ~$300/month-minimum positioning.

The main knock on Reducto is scope: it is fundamentally an ingestion/parsing-and-extraction layer, not an end-to-end document-workflow product. Competitors like Extend argue that Reducto leaves teams to build their own classification, validation, human-in-the-loop review, and continuous improvement, and that dialing in extreme accuracy can require weeks of manual tuning rather than minutes. It is best suited to AI/engineering teams building RAG pipelines or extraction workflows who want a high-accuracy, well-documented API with bounding-box citations, and less suited to non-technical ops teams wanting a turnkey, no-code document-automation suite. Public third-party review coverage (G2/Capterra/Trustpilot) is thin, so most sentiment comes from developer forums and vendor comparisons rather than large aggregate review samples.