Unsiloed AI Is Turning Messy Enterprise Documents Into LLM-Ready Data

Enterprise AI projects tend to fail at a specific, unglamorous step: parsing the documents. Research teams can spend six months building a document pipeline and still watch fewer than ten percent of those projects make it into production, because generic large language model parsers and legacy OCR systems fall apart on the multimodal PDFs, scanned reports, and table-heavy spreadsheets that real businesses actually work with. Unsiloed AI, a San Francisco-based Y Combinator Fall 2025 startup, is building the PDF parsing API that fixes that problem – and its customers already include startups and NASDAQ-listed enterprises.

Unsiloed AI offers an API for parsing multimodal unstructured data. Under the hood, the company has built state-of-the-art vision models that treat documents the way a human analyst does: understanding text, tables, charts, and images together rather than as disconnected layers. That matters because the weakest link in most retrieval-augmented generation systems is not the LLM at the end of the pipeline but the parsing and chunking that happens at the beginning. Poor parsing quietly poisons everything downstream – retrieval accuracy, answer faithfulness, and the overall reliability of AI products that depend on document understanding.

The company was founded in 2025 by Aman Mishra and Adnan Abbas, a team with a striking combination of high-stakes engineering and entrepreneurial experience. Mishra, a graduate of IIT Kharagpur, previously built an ultra-low-latency trading system moving billions of dollars at a hedge fund, served as the founding engineer at a San Francisco AI startup building copilots for firms like Goldman Sachs and Charles Schwab, and launched a peer-to-peer rental platform from his dorm room that scaled to thousands of orders in two months. Abbas is co-founder and CTO and brings an MIT background to the technical architecture of the platform.

Unsiloed AI’s APIs are already parsing hundreds of thousands of documents in production, powering vertical AI solutions across industries where the quality of extracted data directly determines whether an AI product is usable. On public benchmarks, Unsiloed AI consistently outperforms well-known alternatives including LlamaIndex, Gemini, Mistral, and Unstructured.io. That benchmark performance is not a marketing detail – it is the reason enterprise buyers are willing to rip out existing parsing stacks and standardize on a new vendor.

The strategic insight behind the company is that document parsing has been underinvested as a problem relative to its importance. Engineering teams often assume they can solve it with a weekend of OCR integration and some prompt engineering, only to discover months later that the ceiling on their AI product’s accuracy is set by the quality of the data being fed in. Unsiloed AI’s bet is that a specialized vision model trained specifically on the messy reality of enterprise documents – nested tables, multi-column layouts, charts embedded in scanned PDFs, inconsistent formatting – will beat general-purpose solutions for the foreseeable future.

Positioning itself across Artificial Intelligence, B2B, Infrastructure, and APIs, Unsiloed AI is building the kind of developer-facing platform that becomes the default plumbing for a category. Teams integrating the PDF parsing API do not have to become experts in document understanding; they can focus on the AI experience they are building on top of clean, structured, LLM-ready data. That separation of concerns is exactly how successful infrastructure companies grow – by turning a hard, repeated problem into a paid utility.