Problem

AI teams often spend six months or more building document-ingestion workflows, yet fewer than 10% reach production. Traditional OCR and generic LLM parsers struggle with the mixed structure of real-world documents—text, tables, charts, images, forms, and scans—while poor parsing and chunking undermine RAG systems and downstream automation. The economic pain is therefore both engineering cost and unreliable AI output: teams must build and maintain bespoke ingestion infrastructure before their core application can work.

The main use case is high-stakes, document-heavy AI in finance, legal, and healthcare, where a missed table value, broken hierarchy, or lost piece of context can materially affect an answer or workflow. Unsiloed positions the problem as an infrastructure bottleneck for vertical AI applications and RAG pipelines, turning months of ingestion work into a single API call.

Product / Service

Unsiloed AI provides production APIs that ingest, parse, extract, and split multimodal documents, converting PDFs, presentations, spreadsheets, images, and scanned files into clean Markdown and JSON. Its vision-first, layout-aware systems combine computer vision, OCR, and multimodal models to preserve document structure and hierarchy, including tables, charts, forms, and handwritten content. Customers can connect sources such as S3, SharePoint, Drive, Snowflake, and document-management systems, configure schemas, prompts, and confidence thresholds, and chain parsing, extraction, and splitting operations into a production workflow.

The delivery model is developer infrastructure priced by pages processed, with flexible tiers, custom pricing, and SLAs for high-volume pipelines. The benefit is a drop-in document layer for AI agents and LLM applications: instead of passing raw files to a model or maintaining multiple bespoke parsers, teams receive structured, queryable data that is more reliable for extraction, retrieval, automation, and other document workflows.

Market

Unsiloed competes in the B2B AI infrastructure and intelligent document-processing market, specifically the emerging document-parsing and unstructured-data layer for LLMs and AI agents. Its competitive set includes LlamaParse from LlamaIndex, Unstructured.io, Google Document AI, and direct use of foundation-model tooling such as Gemini and Mistral; Reducto and LandingAI are also adjacent competitors offering structured extraction from complex documents. Unsiloed claims better performance on public benchmarks than LlamaIndex, Gemini, Mistral, and Unstructured.io, although those are company claims rather than independently verified results.

The company is not merely pre-product: its YC profile says that its APIs were processing millions of pages of complex documents each week for Fortune 150 banks, NASDAQ-listed companies, and early-stage startups, including more than 10 YC startups, across finance, legal, and healthcare. It is an active YC Fall 2025 company founded by Aman Mishra and Adnan Abbas. Public company databases report a $500,000 Y Combinator funding round, while revenue is not disclosed in the available sources, so traction is best characterized as early commercial usage with undisclosed revenue rather than confirmed profitability or scale economics.