Reducto AI: Document Parsing for LLM Pipelines

Y Combinator-backed document ingestion platform that converts unstructured documents into LLM-optimized structured data, achieving >99% accuracy across 1 billion+ pages processed.

Overview

Reducto AI is a San Francisco-based startup founded in 2023 by MIT alumni Adit Abraham and Raunak Chowdhuri that specializes in document ingestion for LLM workflows. In October 2025, the company closed a $75M Series B led by Andreessen Horowitz, bringing total funding to $108M from a16z, Benchmark, First Round Capital, Y Combinator, and BoxGroup. That round followed a $24.5M Series A led by Benchmark in April 2025 and an $8.4M seed led by First Round Capital in October 2024. The team crossed $1M ARR with only four employees before raising the Series A.

Unlike full-stack document intelligence platforms such as ABBYY, Kofax, or Hyperscience that offer end-to-end workflow automation, Reducto positions itself as a specialized document ingestion layer that converts complex unstructured documents into structured data optimized for LLMs and vector databases. A 2026 API comparison by Lido.app groups Reducto alongside Extend AI as a leader "for LLM-ready output and complex document structures," distinct from the scale-oriented tier of AWS Textract and Google Cloud Document AI. The same review highlights a key limitation: "Reducto is a parsing and extraction API, not a workflow platform. You get structured output, but you're responsible for validation, routing, and system integration."

Monthly processing volume grew 6x between the Series A (April 2025) and Series B (October 2025), reaching approximately 1 billion pages processed. Named AI-native customers include Harvey, Mercor, Rogo, and Scale AI. Enterprise accounts confirmed in case studies include Vanta (compliance automation) and Stack AI (workflow platform). Unnamed accounts include a Fortune 10 company with a 154-day sales cycle involving 14 engineers in the evaluation, a Global Top 5 Hedge Fund, and category leaders in healthcare, insurance, and real estate. All growth figures and accuracy claims are self-reported; no independent analyst validation was found in available sources.

For broader context on this approach, see our guides on PDF to structured data conversion and document processing for RAG. See also the Reducto AI competitive analysis.

How Reducto AI processes documents

Reducto's technical architecture treats documents as images rather than parsing PDFs as structured files. As CEO Adit Abraham explained to First Round Review: "We actually see PDFs as just images, and so we convert them to images. It's not about PDF as a standard, it's about documents and human content overall." The core Parse API runs documents through six vision models, combining traditional computer vision with vision-language models (VLMs) to produce LLM-ready output from 30+ document formats including PDFs, Excel, and PowerPoint.

The architecture reflects a deliberate bet: rather than competing on base OCR accuracy alone, Reducto layers an Agentic OCR framework on top. This multi-pass pipeline automatically detects and corrects parsing errors without human intervention, running multiple vision model passes to verify extraction results on complex documents where traditional OCR fails. The trade-off is speed: the Lido.app comparison notes the multi-pass approach is slower than single-pass competitors but produces higher accuracy on difficult documents such as handwritten forms, degraded scans, and complex layouts. The roadmap extends this approach further with agentic chart extraction and agentic extraction review.

Unlike enterprise APIs from AWS, Google, and Azure that flatten document structure for database ingestion, Reducto preserves headings, sections, tables, and lists in LLM-consumable formats. This architectural choice directly targets retrieval-augmented generation (RAG) pipelines where structure and context preservation affect language model reasoning quality. On RD-TableBench, an open-source table parsing benchmark Reducto created with 1,000 complex table images annotated by PhD-level labelers, the platform records over 20 percentage points better performance than traditional text-only parsers. No independent benchmarks comparing Reducto against AWS Textract, Azure Document Intelligence, or Unstructured were found in available sources.

Beyond parsing, the platform exposes additional endpoints: split, extract, and an unstructured document editing API. In July 2025, Reducto expanded into document generation with Reducto Edit. The company also released RolmOCR, an Apache 2.0 licensed OCR model with 8.29B parameters that recorded 190,046 downloads in its first month. The open-source RolmOCR model and the RD-TableBench benchmark are strategic moves to establish developer credibility: the Lido.app review notes RolmOCR gives "developer community credibility that most enterprise vendors lack."

Reducto Studio provides an interactive workspace for building, evaluating, and deploying document pipelines, serving as the interface layer connecting API endpoints into end-to-end workflows. Teams evaluating open-source alternatives for table extraction benchmarking may also reference document parsing benchmarks for independent methodology comparisons.

Use cases

Healthcare document processing

Reducto achieved 99.24% extraction accuracy in clinical SLAs on real patient cases, demonstrating performance in accuracy-critical regulated environments with complex medical documentation. The platform's BAA support for HIPAA and zero data retention policy address the data handling requirements common in this sector. Our healthcare claims automation guide covers industry-specific implementation patterns.

Insurance claims processing

Insurance sector deployments report up to 16x faster claim reviews with improved auditability, processing policy documents, claims forms, and supporting documentation at enterprise scale. The combination of burst handling at 1/10/100+ QPS tiers and SOC 2 Type II certification addresses the volume and compliance requirements typical of large carriers.

Legal and compliance

Enterprise customers including Harvey use Reducto for legal document processing, contract analysis, and compliance documentation where extraction accuracy and auditability are mission-critical. Vanta, the compliance automation platform, replaced AWS Textract with Reducto after a head-to-head comparison for security questionnaire automation and compliance evidence evaluation. "We ran a side-by-side comparison. Reducto's quality was higher, and it was faster. It was a no-brainer to switch," said Ignacio Andreu, Head of Vanta AI. Reducto built a custom document filling capability (edit endpoint) in partnership with Vanta, moving from prototype to production within one month.

We ran a side-by-side comparison. Reducto's quality was higher, and it was faster. It was a no-brainer to switch.

Ignacio Andreu, Head of Vanta AI

Financial services

A Global Top 5 Hedge Fund is among Reducto's unnamed enterprise accounts, alongside customers in real estate. The extract endpoint and structured JSON output support financial document workflows including earnings filings, fund documents, and loan packages, where data fidelity determines downstream analytical quality. Stack AI customers have processed over 5 million documents through Reducto's parsing layer for knowledge bases, search workflows, and M&A due diligence agents handling financial statements, tax filings, and contracts. "When we discovered Reducto, we found it was one of the best in the market. The most flexible, easiest, and straightforward to have work with complex data," said Bernard Aceituno, Co-Founder of Stack AI.

Teams building investment research pipelines may also evaluate Parsewise, a Y Combinator-backed platform targeting similar extraction use cases for investment teams and underwriters. Financial services teams requiring deeper document analytics and research automation may also consider Acuity Knowledge Partners, which serves 800+ institutions with agentic AI for document-intensive research workflows.

Technical specifications

Resources

• Company website
• Documentation
• Vanta case study
• RolmOCR model
• Elasticsearch integration guide
• AWS Marketplace listing
• Trust center
• First Round Review profile
• Reducto AI competitive analysis

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Company information

Reducto AI was founded in 2023 by Adit Abraham and Raunak Chowdhuri, both MIT alumni who went through the MIT delta v accelerator and Y Combinator (W24) before raising institutional funding. The company is headquartered in San Francisco. Total funding stands at $108M across three rounds: $8.4M seed (First Round Capital, October 2024), $24.5M Series A (Benchmark, April 2025), and $75M Series B (a16z, October 2025). Abraham told First Round Review the company is "supply constrained, not demand constrained", having hit $1M ARR with just four people before scaling the team to roughly a dozen employees by mid-2025 (current headcount unverified).

"Documents contain some of the most valuable data in most industries — from healthcare to finance to logistics. Yet until now, they've been a bottleneck for making AI useful for real enterprise use cases," Abraham said at the Series B announcement. "Our vision is to build the trusted layer that connects messy, real-world data with language models." Jennifer Li, General Partner at Andreessen Horowitz, described Reducto as "a magic ingredient that modern AI companies build with when it comes to large scale document workloads," citing the team's early bet on vision-language models and its enterprise infrastructure as the basis for the firm's investment.

The AWS Marketplace listing, announced alongside the Series B, is a procurement play as much as a product milestone: enterprise buyers with committed AWS spend can now route Reducto purchases through existing contracts, removing a common friction point in vendor evaluation. At $0.015 per credit, Reducto's pricing sits 10x above AWS Textract's $0.0015/page for basic text detection, reflecting the premium for structured LLM-ready output from complex document types. The company explicitly positions Reducto against Textract as the higher-accuracy alternative, a direct challenge to Textract's incumbency advantage inside AWS environments.

Reducto's trust center documents four deployment models: multi-tenant cloud, customer VPC, on-premises, and fully air-gapped installations with zero outbound connectivity. Subprocessors include AWS, OpenAI, Anthropic, Sentry, PostHog, Google Cloud, and Modal Labs (all US-based). The reliance on OpenAI and Anthropic as subprocessors means Reducto's vision model pipeline depends on third-party AI providers, a potential concern for customers evaluating vendor lock-in and data sovereignty. Teams evaluating open-source alternatives for LLM pipeline ingestion may also consider Unstract, which takes a no-code approach to the same document-to-structured-data problem with hallucination mitigation built into its extraction layer.

Transfer of personal data: Reducto processes and stores information in the U.S. and other countries. Use of their services authorizes transfer of personal information across national borders in accordance with applicable laws. See Reducto Terms of Service for details.