# Reducto

> Agentic document parsing and extraction platform for AI pipelines.

Reducto is a document-processing API designed to parse, split, extract, and edit data from complex documents like PDFs, spreadsheets, and scanned forms. It combines OCR with vision language models to preserve document structure, tables, and formatting for downstream LLM workflows. It also supports on-premise or VPC deployments for strict compliance needs.

Website: https://reducto.ai/

## What it is for

- Parse complex multi-column PDFs and financial tables into LLM-ready formats
- Extract structured JSON data from invoices and onboarding forms using defined schemas
- Split large combined document batches into individual files using layout heuristics
- Fill in blanks and form fields automatically without fixed templates

## Features

- Multi-pass OCR combined with vision language models (r-1 model)
- Document splitting with layout-aware heuristics
- Schema-level structured data extraction
- Template-free automated form editing and filling
- Granular bounding-box citations for extracted data
- Multilingual support across 100+ languages including mixed text
- Python and Node.js SDK support
- On-premise / self-hosted VPC deployment option

## Pricing

Freemium. Free trial tier available; pay-as-you-go standard tier starts around 1¢ per page; custom pricing for Enterprise.

## Details

- Type: Web app
- Category: [AI](https://xiry.app/topics/ai.md)
- Open source: no
- Platforms: API, Web, Self-hosted
- For: Developers and AI engineers building RAG pipelines and document automation workflows.
- Alternative to: LlamaParse, AWS Textract, Unstructured, Google Document AI
- Tags: ocr, document parsing, data extraction, llm, rag, pdf

## Why it was saved

- [@d__raptis, 28 October 2025](https://x.com/d__raptis/status/1983100247028183179): Real product shown in the video — an AI document-processing API worth knowing about.

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- [8 Ways to Improve RAG Applications](https://xiry.app/resources/8-ways-to-improve-rag-applications): Practical architectural strategies to fix common failure modes in RAG systems (Tip, Free)
- [AnythingLLM](https://xiry.app/resources/anythingllm): Private, on-device AI assistant to chat with documents and local models. (Desktop app, Open source)
- [CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning](https://xiry.app/resources/clara-bridging-retrieval-and-generation-with-continuous-late): RAG framework using continuous latent reasoning and embedding-based context compression (Article or course, Free)
- [GPT4All](https://xiry.app/resources/gpt4all): Desktop app to run open-source language models locally and completely offline (Desktop app, Open source)
- [memary](https://xiry.app/resources/memary): Open-source persistent memory layer for autonomous AI agents using knowledge graphs (GitHub repo, Open source)

Updated 10 October 2026. Page: https://xiry.app/resources/reducto

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