Problem

Enterprise knowledge is trapped in messy, unstructured files such as PDFs, spreadsheets, scans, contracts, and forms. Reducto’s research materials say nearly 80% of enterprise data is in these formats, while manual processing consumes dozens of hours each week. Building a reliable in-house pipeline can take months, require hundreds of hours of labeling and model maintenance, and still suffer from accuracy problems as document layouts change; costs are consequently difficult to predict at scale.

The core use case is turning difficult real-world documents into trustworthy, machine-readable inputs for LLMs, search, analytics, and workflow automation. This is especially valuable in finance, legal, healthcare, insurance, and other settings where extracting a wrong number, clause, or patient detail can undermine the entire downstream workflow.

Product / Service

Reducto provides an agentic document-intelligence platform through developer APIs and related tooling. It can parse, extract, split, classify, and edit more than 30 file types, including documents containing complex tables, charts, handwriting, scans, and spreadsheets. Its hybrid architecture combines traditional computer vision, custom models, frontier vision-language models, and multi-pass Agentic OCR that reviews and corrects parsing errors. The resulting structured, citation-ready data is designed to be consumed directly by LLM applications and automated workflows.

The delivery model is enterprise-oriented but supports both engineering and no-code workflows through APIs, SDKs, a CLI, and Studio. Customers can use Reducto in the cloud or deploy models in their own cloud, on-premises, or fully air-gapped environments, with security features including SOC 2, HIPAA, and zero data retention. The benefit is to outsource the difficult ingestion layer while gaining higher-fidelity outputs, faster implementation, continuous model improvements, and infrastructure that can scale with document volume.

Market

Reducto competes in the document intelligence and AI document-ingestion market, positioned as infrastructure between unstructured enterprise files and downstream LLM, RAG, and automation systems. Its alternatives include developer-first platforms such as LlamaParse and LlamaExtract; cloud-native services such as Azure Document Intelligence, Google Document AI, and Amazon Textract; and established enterprise platforms such as ABBYY Vantage. The competitive battleground is accuracy on messy documents, traceability and citations, deployment flexibility, and high-volume reliability.

The company shows substantial commercial traction rather than looking like a pre-revenue prototype. In October 2025, Reducto announced $108 million in total funding, including a $75 million Series B led by Andreessen Horowitz. It reported customers including Harvey, Mercor, Rogo, Scale AI, and Vanta, as well as a Fortune 10 company and a global top-five hedge fund; monthly processing volume had grown sixfold in six months, and the platform had processed more than one billion pages. Public sources reviewed do not disclose revenue, but they do indicate broad enterprise adoption and significant usage at scale.