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Reducto: Making Human Data LLM-Ready With State-of-the-Art Accuracy

Reducto just raised $24.5M in Series A funding to help enterprises unlock unstructured data with near-perfect accuracy. AI teams today are bottlenecked by messy, real-world documents—so Reducto built the most accurate parsing pipeline in the industry. By combining vision-language models with agentic workflows, Reducto turns complex PDFs and scanned documents into structured, LLM-ready data. Now trusted by companies like Scale AI, Vanta, and top AI teams, Reducto has parsed over 250 million pages and is expanding into full end-to-end pipelines: document splitting, classification, structured extraction, and more. With their new Agentic OCR framework, they’re pushing toward human-level accuracy—automating what used to take teams days, in seconds. YC Partner Diana Hu recently sat down with the Reducto founders to talk about how they got here, their founding story, and the kind of company they are building. Learn more about Reducto at https://reducto.ai. Apply to Y Combinator: https://ycombinator.com/apply Chapters (Powered by ChapterMe) - 00:00 - Data-driven AI for large enterprises 01:17 - Document management 03:04 - Simplify PDF processing for companies 03:59 - Aha moment for PDF extraction, interesting approach 05:02 - NLP-based PDF extraction for enterprise apps 06:56 - Great data, exciting use cases 08:10 - Best places for customer approaches 08:48 - Closing a Fortune 25 deal in just two months 11:21 - Data-driven AI for high-quality documents 13:19 - Reductos AI-focused infrastructure attracts top companies 15:18 - Quality of data, results, support

Diana Huhost
Apr 30, 202515mWatch on YouTube ↗

At a glance

WHAT IT’S REALLY ABOUT

Reducto turns messy enterprise documents into accurate LLM-ready structured data

  1. Reducto converts complex, messy enterprise documents (claims, health records, financial statements) into clean structured data optimized for LLM workflows like RAG and summarization.
  2. The company emerged from an initial long-term LLM memory project after customers repeatedly asked for better handling of uploaded files, revealing PDF ingestion as a major bottleneck.
  3. Their key technical shift was treating document parsing as a computer-vision problem—“reading like a human”—rather than relying on brittle heuristics and format-specific rules.
  4. Customers report major downstream gains from simply swapping ingestion providers, including large LLM accuracy jumps and formerly impossible features such as reliable processing of scanned and handwritten documents.
  5. Reducto rapidly won large enterprise deals (including a Fortune 25) by outperforming internal document-processing teams and investing heavily in high-quality evaluation data, benchmarks, and iteration pipelines.

IDEAS WORTH REMEMBERING

5 ideas

Document ingestion quality is a primary limiter of LLM product performance.

Customers see significant end-task accuracy improvements (reported up to ~30%+) by improving extraction/layout/chunking before the model ever reasons, because the LLM can only work with what it’s given.

PDF processing is “schlep work” that many teams underestimate until it blocks shipping.

Even strong AI teams end up spending disproportionate time on parsing and post-processing because off-the-shelf tools often fail on real-world variability, yet few companies want to specialize in it.

A vision-first approach can generalize better across the long tail of document formats.

Reducto reframes parsing as understanding visual structure (spacing, hierarchy, layout) like a human reader, avoiding brittle rule sets tied to specific templates or PDF standards.

Modern VLMs unlock extraction cases that traditional OCR struggles with.

Handwriting, scanned documents with missing metadata, checkboxes, and unusual markings (e.g., highlighted/circled table cells) become tractable when combined with CV layout models and targeted model orchestration.

“Be the ingestion team” is a compelling wedge into the AI application stack.

By abstracting ingestion, Reducto lets application teams focus on product logic and reasoning layers while still reaching the quality bar needed for enterprise deployments.

WORDS WORTH SAVING

5 quotes

We help people take their really, really complicated documents… and turn that into clean structured data… primarily LLM-based use cases like RAG and summarization.

— Reducto founder

None of these AI application layer companies want to be PDF processors… we try to be the ingestion team for the companies that we work with.

— Reducto founder

We turned PDF processing… into a computer vision problem… parse and understand these documents the way humans do.

— Reducto founder

People will often see end LLM accuracy improvements just from swapping the ingestion provider.

— Reducto founder

The quality of your data is the quality of your end outputs and results.

— Reducto founder

LLM-ready data ingestion for enterprisesPDF/document extraction as a bottleneckVision-first layout understanding vs heuristicsDownstream LLM accuracy improvements (RAG, summarization)Handling long-tail document artifacts (scans, handwriting, highlights, checkboxes)Enterprise sales motion competing with internal teamsDatasets/benchmarks and in-house data engineExpansion to spreadsheets, images, slidesHiring profile: scrappy, detail-obsessed engineers

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