What

Reducto is AI document parsing and extraction software designed to convert unstructured documents into structured, LLM-ready data. It combines traditional layout-aware computer vision with vision-language models in a multi-pass pipeline to capture document structure, interpret context, and correct OCR mistakes.

The product appears positioned for teams building production AI workflows where document accuracy is critical, including finance, healthcare, insurance, and legal use cases. Its core workflow covers parsing documents, splitting multi-document files, extracting schema-level fields, and editing/filling form elements through APIs rather than manual template setup.

Features

Layout-aware parsing with Agentic OCR correction: Reads document layout, structure, and meaning, then reviews and corrects outputs in real time to improve edge-case accuracy.
Automatic document splitting: Separates long or combined files into usable document units using layout-aware heuristics, reducing manual preprocessing.
Schema-level data extraction: Pulls structured fields from documents like invoices, onboarding forms, and disclosures so data can be routed into downstream systems.
Template-free document editing/fill: Detects blanks, tables, and checkboxes dynamically and fills them without predefined bounding boxes, including scanned and multi-page files.
Broad input and language support: Processes PDFs, images, spreadsheets, slides, and multilingual documents (100+ languages), including low-quality scans, faxes, and handwriting.
LLM-oriented processing utilities: Provides chunking, embedding optimization, and related capabilities intended to make parsed content easier to use in LLM pipelines.

Helpful Tips

Validate performance by document class (e.g., financial statements, forms, handwritten files), since accuracy can vary by layout complexity and scan quality even with strong OCR pipelines.
Start implementation with one high-value workflow (such as extraction from a specific form set), then expand once schema coverage and error handling are stable.
If operating in regulated environments, confirm deployment model, security documentation, and SLA details early; the page states SOC2/HIPAA claims and in-environment deployment options.
Design human-review checkpoints for low-confidence fields in early rollout, especially where downstream business decisions depend on exact values.
Evaluate parsing, splitting, and extraction together as one pipeline, because improvements in earlier stages directly impact final field quality.