Reducto 聯合創辦人兼 CEO Adit Abraham 專訪

Adit Abraham is the Co-Founder and CEO of Reducto, where he focuses on turning complex enterprise documents into reliable data for AI applications. Before founding the company with fellow MIT graduate Raunak Chowdhuri, he studied computer science at MIT, conducted machine learning research at the MIT Media Lab, and worked on Ads and Search as a product manager at Google. Reducto emerged from the founders’ efforts to build memory tools for large language models, when unreliable document ingestion became a recurring obstacle. Abraham’s direct work with early customers helped shape the company’s emphasis on accuracy, security, deployment flexibility, and predictable performance in production.

Reducto develops document intelligence infrastructure that helps AI teams convert PDFs, scans, spreadsheets, and other files into structured, usable information. Its platform combines optical character recognition, computer vision, and vision-language models to interpret text, tables, figures, formatting, and complex page layouts. Alongside parsing, it offers tools for structured extraction, document splitting, classification, and editing, with citations that connect extracted information to its source. Customers include Harvey, Scale AI, Vanta, Mercor, and Rogo. The company’s work spans retrieval-augmented generation, search, and document workflows in fields such as finance, healthcare, and law, where accurate ingestion and traceable results are essential.

Reducto grew out of challenges you and Raunak Chowdhuri encountered while building memory tools for large language models. What specifically broke with existing document-processing tools, and when did you realize the parsing problem itself was large enough to build a company around?

We were trying to give models access to context from documents so we added a file uploading feature, but we kept running into problems with quality of the processing. We tried all the existing tools out there at the time, but none of them met our standards when it came to working with real-world messy documents that people actually uploaded.

So we built our own parsing solution originally as a feature of our LLM memory product, but then saw amazing pull from developers who’d run into this problem themselves as well and loved our solution. Some teams were spending weeks building and maintaining their own ingestion pipelines because the available tools couldn’t handle their documents reliably. Those conversations made the opportunity much clearer to us, because despite model intelligence getting better and better, clean and accurate context still made a huge difference.

PDFs are technically designed for displaying information rather than making that information machine-readable. What makes complex documents, particularly those containing tables, charts, multi-column layouts, or embedded images, so difficult for AI systems to understand reliably?

When we as humans read a document, we take a lot of our natural understanding for granted – we can infer relationships between almost instantly, because we can see that a number belongs under a particular column heading, that text continues in another column, or that a caption refers to the figure above it.

AI models actually really struggle with this, as PDFs aren’t designed to retain this type of relational information or metadata; they’re essentially very data-dense images. A parser has to reconstruct those relationships from the page, and the problem gets much harder with merged cells, nested tables, charts, scans and unusual layouts. Because we focus on this document understanding problem, we can reliably process the long-tail of documents that AI models tend to hallucinate and mess up on.

Reducto combines computer vision, optical character recognition, and vision-language models in its document-processing pipeline. How do these different technologies work together, and where do traditional OCR systems still tend to fall short?

Traditional OCR has been around for decades, and was built to read documents that contained clean, legible text on a character by character basis. This easily falls apart when you introduce documents that are handwritten, scanned, skewed, or tabular, which tends to be the majority of enterprise data.

Reducto pioneered adding vision language models to the document processing space with “Agentic OCR” because we saw the potential for VLMs to understand more than just the pure text in documents – real document intelligence needs context, accuracy, and multi-modal abilities. Our newest r-1 parsing model combines the powers of Agentic OCR with features like formatting, citations, and layout detection into one single pass.

That extra work matters because even a parser with very high overall accuracy can cause a serious problem when the small amount of information it misses contains the figure, clause or table entry someone actually needs.

As AI models become increasingly multimodal and capable of directly interpreting images and documents, how do you see the role of specialized document intelligence platforms evolving? Could foundation models eventually perform much of this work themselves, or will dedicated infrastructure remain necessary?

We actually are betting on the fact that AI models will only improve and become faster and better – with new models constantly coming out, we stay up to date on evaluating their weaknesses, and integrate their strengths into our own pipelines to offer the best performance overall to our customers. Reducto wants to act as your ingestion team, helping build with you as you grow.

Enterprises still have the unique challenge of needing not just performance, but scalable throughput, governance, and verifiability at production-scale which is still difficult for foundation models today.

We recently tested this with the GDP.pdf benchmark, which showed that frontier models still struggle to reason on complex real professional documents and scored under 15% to 25% on strict task-level pass rates. By letting Reducto parse these documents first and then passing the structured data to the frontier models, macro accuracy improved by nine percentage points while reducing reasoning tokens by 13 percent compared to the models consuming the documents directly.

This shows that there is still immense value to dedicated ingestion infrastructure as most enterprise data is still unsaturated.

Many enterprise AI applications ultimately depend on retrieval-augmented generation or agentic systems accessing information buried inside internal documents. How much of the reliability problem in these systems actually begins upstream with document ingestion and parsing?

A significant amount starts during ingestion – teams often discover the problem much later because the bad answer appears at the end of the pipeline. If a parser drops a row from a table, mixes the reading order of two columns or misses a figure, retrieval cannot recover information that never even entered the database.

We commonly see our customer’s end users’ experience improve dramatically after swapping their ingestion with Reducto, as RAG and search systems return better results more consistently. Harvey, for example, saw their OCR-related customer tickets drop drastically and product usage multiply after implementing Reducto as their processing layer.

Reducto has expanded beyond parsing into structured extraction and broader document workflows. What does the next layer of document intelligence look like once a system can accurately understand the underlying structure of a document?

Data ingestion usually represents just the first step inside a much larger business process. A company might receive a 500-page file and need to identify the documents inside it, classify them, extract specific information, compare that information with another system and send unusual cases to a specialist for review. We’ve spent 2 years training the best document understanding models on the market, and now we’re spending more time focusing on how to streamline what comes after.

Once you can reliably understand the contents and structure of a document, you can automate more of the work people currently spend their time assembling, checking and moving between systems. Almost every high-stakes business workflow can be abstracted into steps that involve either humans reading from, making decisions, and writing into other documents – we’re looking to become the default way in which humans or agents interact with document data in the future.

You have customers ranging from AI-native companies such as Harvey, Vanta, Scale AI, Mercor and Rogo to major financial institutions and Fortune 10 companies. How different are the document-processing requirements of an AI startup compared with a large regulated enterprise?

AI-native companies tend to move quickly, and their engineering teams want an API that handles difficult documents without forcing them to build and maintain their own processing stack. Large enterprises care about the same accuracy, but they often have additional requirements around where data lives, how they deploy the software, what happens when volume spikes and whether users can trace an extracted value back to the original page. Scale AI, for example, uses Reducto in government environments where confidentiality and on-premises deployment matter, while Harvey evaluated us across accuracy, reliability, cost, latency and failure modes before moving into production in roughly six weeks. Working with both types of customers has pushed us to focus as much on how Reducto performs under real production requirements as on how accurately it handles an individual document.

Reducto reportedly reached more than $5 million in annual recurring revenue largely through founder-led sales before building a dedicated sales organization. What did speaking directly with those early customers teach you about the problems developers were actually trying to solve?

Those conversations taught us to spend less time talking broadly about document processing and more time understanding what customers were actually trying to accomplish. Early sales calls often became working sessions where a customer shared the documents that broke their pipeline, and we’d dig into what happened downstream when extraction failed. In healthcare, that meant seeing how a dropped table row or misread unit in a lab report could delay a claim or leave a clinician with an incomplete patient history. Those stakes reset our bar for accuracy: a strong benchmark score means little if it hides the errors that matter most. Customers needed accuracy, but also reliability, security, deployment flexibility and predictable performance at scale, and hearing that directly shaped what we built.

Reducto now processes billions of pages per month. What engineering challenges emerge at that scale, particularly around latency, accuracy, cost and maintaining consistent results across very different document types?

I’ve already lost track of the number of pages we process, so even a small inefficiency can become expensive very quickly. Adding unnecessary inference to every page increases both cost and latency, while optimizing too aggressively for speed can hurt accuracy on difficult documents, which means we spend a lot of engineering effort determining how much processing each page actually requires.

With r-1, we improved parsing accuracy while reducing latency and bringing the price to one cent per page, and we’re also working toward more automatic routing so that a simple page doesn’t consume the same resources as one containing a complicated table or figure. At our current volume, those decisions affect millions of pages every day, which makes efficiency part of the core engineering problem.

Looking several years ahead, as AI agents increasingly interact with contracts, financial records, research papers, presentations and other enterprise documents, what do you believe the ideal interface between AI systems and the world’s unstructured information will look like?

Today’s AI agents are fundamentally text-native. They’re very good at reasoning over clean, digital information, but the real world still runs on PDFs, spreadsheets, presentations, scans, contracts and financial records. Over time, I think agents should be able to interact with those documents as naturally as they interact with a text file today.

Developers shouldn’t have to think deeply about OCR, parsing, model selection or document structure. An agent should be able to receive any file, understand what’s inside it, trace important information back to the source, and use that information reliably in whatever task it needs to complete. The infrastructure underneath should determine how to process the document, choose the right models and escalate uncertainty when human judgment is actually required.

That’s ultimately the division of labor I think we want: machines handling repetitive document work, while people spend their time on the decisions that require context, institutional knowledge and judgment. Documents contain an enormous amount of the information businesses run on, so making that information truly agent-native will be a major unlock for how useful AI becomes inside real business processes.

Thank you for the great interview, readers who wish to learn more about turning complex documents into reliable data for AI applications should visit Reducto.