Unsiloed offers a unified document layer that converts raw enterprise files—such as contracts, financial reports, and medical records—into structured, LLM‑ready data via a single API. Its dual‑stream vision model parses, extracts domain‑aware fields into JSON, and splits documents into indexed chunks, integrating with existing storage systems and delivering results for AI agents, vector databases, or data warehouses.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often store critical information in unstructured documents such as contracts, financial reports, and medical records, making it difficult to query, analyze, or feed the data into AI systems. Traditional document processing tools struggle with varied layouts, tables, and domain-specific terminology, leading to low accuracy and high manual effort.
Solution
Unsiloed provides a unified document layer that transforms raw files into structured, LLM‑ready data. Its platform offers three core capabilities—Parse, Extract, and Split—delivered through a single API that can be used for rapid prototyping or production‑scale pipelines. A dual‑stream vision model processes both textual tokens and visual layout information in parallel, while cross‑attention fuses content and structure for accurate understanding. A domain‑specific decoder, trained on millions of real enterprise documents, outputs schema‑conditioned JSON with cross‑field constraints, ensuring totals and references are consistent. The service integrates with existing storage systems (S3, SharePoint, Drive, Snowflake, etc.) and delivers results in JSON, Markdown, or structured fields for downstream AI agents, vector databases, or data warehouses.
Target Audience
Primary customers are large enterprises and organizations that need to automate extraction of structured data from heterogeneous document collections, such as banks, legal firms, healthcare providers, and regulatory agencies.
Features
- Parse documents to generate tokenized representations that capture both text and visual layout
- Extract fields into JSON using a domain‑aware decoder that understands legal, financial, healthcare, and regulatory ontologies
- Split multi‑document files and long documents into indexed chunks, preserving parent‑child relationships for clauses and preambles
- Dual‑stream vision architecture with separate data and layout streams and cross‑attention fusion for content‑structure reasoning
- Attention‑guided heatmaps that highlight relevant regions, improving interpretability and debugging
- Seamless integration with existing data sources (S3, SharePoint, Google Drive, Snowflake, DMS) and output formats for LLMs, AI agents, and vector stores