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API for parsing multimodal unstructured data
AI teams spend 6+ months building document workflows, yet fewer than 10% ever reach production. Generic LLM parsers and OCR collapse on multimodal documents with text, tables, images, and charts. Poor parsing and suboptimal chunking cripple RAG pipelines and downstream automation. Unsiloed AI has built state-of-the-art vision models which serves as the infrastructure layer for turning unstructured data into structured, queryable, and LLM-ready assets. Our APIs are already parsing hundreds of thousands of documents for startups and NASDAQ-listed enterprises, powering vertical AI solutions across industries. On public benchmarks, Unsiloed AI consistently outperforms solutions from LlamaIndex, Gemini, Mistral, and Unstructured.io among others.
Unsiloed AI provides an API to parse multimodal unstructured documents, converting PDFs, images, and spreadsheets into structured JSON and Markdown for LLMs and AI agents. It emphasizes preserving document structure and domain-specific schemas, enabling production-grade extraction and reasoning over complex documents.
The platform offers a document layer with three core capabilities: parse, extract, and split. It processes PDFs, scans, images, and spreadsheets to produce LLM-ready JSON or Markdown, preserving tables, figures, hierarchy, and layout. It uses a dual-stream vision model (data stream for tokens/numbers/entities and layout stream for image tokens and structural cues) with a domain-specific decoder to output schema-conditioned results. Users can connect data sources (S3, SharePoint, Drive, Snowflake, DMS), configure schemas, prompts, and confidence thresholds, and chain extractor, splitter, and parser modules for prototype or production-scale pipelines. Outputs support structured fields, parent-child relationships, and confidence scores, with private VPC/on-prem options available.
Who it’s for: Enterprises that work with large volumes of unstructured documents (PDFs, scans, spreadsheets) and require LLM-ready outputs for downstream AI agents, RAG, or analytics.
Backed by Y Combinator; mentions growth benchmarks, enterprise-focused deployment options, and a pricing/tiering approach for high-volume pipelines; indicates traction and venture backing.
Co-founder at Unsiloed AI • IIT Kharagpur Previously built an ultra low-latency trading system moving billions at a hedge fund. Founding Engineer (#1) at an SF-based startup building AI copilots for firms like Goldman Sachs and Charles Schwab. Launched a P2P rental platform from my dorm room, scaling it to thousands of orders within 2 months of operation.
Co-founder & CTO at Unsiloed AI • MIT • IIT Kharagpur Built multi-modal models deployed at a Fortune 10 company. Was building autonomous navigation systems at Mercedes Benz. Launched India’s first Web 3.0 audio app while in college, scaling it to thousands of users within a month.
We build APIs to parse multimodal unstructured data and convert it into LLM-ready formats. Our vision is to make documents as computable and queryable for AI Agents as your data sitting in an RDS.
Unsiloed AI builds APIs that ingest multimodal unstructured documents (PDFs, slides, Word, tables, images) and converts them into structured Markdown and JSON for downstream LLMs and AI Agents, with on-premise options and domain-specific decoding. They demonstrate high-accuracy extraction, chunking, and dual-stream representation to preserve data content and layout across finance, legal, and healthcare use cases.
Open-Source Reinforcement Learning (RL) & Fine-tuning for LLMs.
Production-grade unstructured document extraction
