Reducto
High-accuracy document ingestion API — parsing, agentic OCR, table and figure extraction, and splitting that turns messy PDFs into LLM-ready data for RAG.
Reducto is a hosted document ingestion API for production RAG and document pipelines. It parses PDFs, images, spreadsheets, and slides into LLM-ready data — combining specialized OCR with vision-language models for accurate tables, figures, and layout — plus Extract, Split, and Edit APIs and bounding-box citations for traceability.
Reducto is a high-accuracy document ingestion API built for production RAG and document pipelines. Its core Parse API reads PDFs, images, spreadsheets, slides, scanned pages, and handwritten content, returning LLM-ready structured output that preserves layout, reading order, tables, and figures. Around parsing, it offers Extract (schema-based field extraction), Split (separating multi-document files into individual units), and Edit APIs.
What sets Reducto apart is its accuracy on complex and financial documents. Rather than relying on OCR alone, it combines specialized OCR with vision-language models in a multi-pass, self-correcting "agentic OCR" approach that catches and fixes recognition errors — which is where template-based or single-pass tools tend to fall apart on dense tables, multi-column layouts, and statements.
It is a closed-source hosted API, not a library you self-host (though it offers on-prem and VPC deployment on its enterprise tier). Reducto publishes open-source client SDKs for Python, Node, and Go under Apache-2.0, but the parsing engine itself runs as a managed service.
Highlights
- Agentic OCR — a multi-pass, self-correcting OCR + VLM pipeline that improves robustness on previously unseen, messy documents.
- Complex table and figure extraction — strong handling of dense financial statements, multi-column layouts, and figures.
- Bounding-box citations — output includes source coordinates, so retrieved chunks stay traceable back to the original page.
- Full ingestion suite — Parse, Extract, Split, and Edit across 30+ file types and many languages, through one API.
Good to know
Reducto is freemium: a free tier with included credits, usage-based Standard and Growth plans, and an Enterprise tier adding VPC/on-prem deployment, custom SLAs, and security commitments (SOC 2, HIPAA, zero data retention, BAAs). It's most useful when document parsing is the accuracy bottleneck in a RAG pipeline — pair it with how-rag-works for the surrounding retrieval design and a chunking-strategy-optimizer for tuning the chunks it produces.
Frequently asked questions
- What is Reducto?
- Reducto is a hosted document ingestion platform for AI teams. Its Parse API turns PDFs, images, spreadsheets, and slides into LLM-ready structured data — capturing layout, reading order, tables, and figures — and it adds Extract (schema-based data extraction), Split (separating multi-document files), and Edit APIs. It's aimed at production RAG and document pipelines where accuracy on complex or financial documents matters.
- Is Reducto free?
- Reducto is freemium: a free tier with included credits, then usage-based paid plans (Standard and Growth) and an Enterprise tier for VPC/on-prem deployment, custom SLAs, and security agreements like ZDR and BAAs. The product is a closed-source hosted API, though Reducto publishes open-source client SDKs (Python, Node, Go) under Apache-2.0.
- How do I use Reducto?
- Sign up for an API key and call the REST API directly or via the official Python, Node, or Go SDK. Submit a document to the Parse API to get back structured, chunked output with bounding-box citations, or use Extract to pull fields against a schema. The parsed output drops into a retrieval pipeline or vector store for RAG.
Related
- How RAG Actually Works: Ingestion, Chunking, Retrieval & RerankingA clear, practical walkthrough of the retrieval-augmented generation pipeline — what each stage does, where it fails, and how the pieces fit together.
- Multimodal Document ExtractorExtract structured data from documents and images with a vision-language model — define the target schema, prompt the VLM to fill it from the page (invoices, forms, receipts, statements, IDs), and verify critical fields against the source. Use when you need reliable structured output from messy, varied, or scanned documents that defeat template-based OCR.
- Multimodal RAG over PDFs, Scans & Charts: Two Approaches That Actually WorkRAG over visual documents — PDFs, scans, charts — where text-only extraction loses tables and layout. Parse-then-text vs embed-the-page-image, with trade-offs.
- Chunking Strategy OptimizerFind the chunking strategy and size that maximizes retrieval quality for a specific corpus, by sweeping configurations against a fixed eval set instead of guessing. Use when RAG answers miss obvious content, when standing up a new corpus, or when picking chunk size/overlap.