Unsiloed AI offers a vision‑language platform that converts multimodal, unstructured documents—including PDFs, spreadsheets, slides, and images—into structured LLM‑ready formats such as JSON and Markdown. The system employs a dual‑stream model and domain‑aware decoder to extract text, tables, and visual hierarchy, then creates hierarchical indexes for fast semantic retrieval, with secure on‑premise, air‑gapped, or cloud deployment and confidence‑scoring for high‑accuracy outputs.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprise organizations generate massive volumes of multimodal, unstructured documents—PDFs, spreadsheets, slides, and images—that are difficult to ingest into large language models. Building reliable parsing pipelines often takes months, and fewer than 10 % of in‑house solutions reach production, limiting automation and decision‑making speed.
Solution
Unsiloed AI delivers a vision‑language platform that converts unstructured, multimodal documents into structured, LLM‑ready formats such as JSON and Markdown. Its proprietary dual‑stream vision model captures text, tables, numbers, and visual hierarchy, while a domain‑aware decoder maps extracted content to enterprise ontologies, preserving context and nesting. Hierarchical indexing creates parent‑child chunk relationships for efficient retrieval and downstream reasoning. The service can be deployed on‑premise, in air‑gapped environments, or as a cloud‑native offering, ensuring data never leaves the customer’s control. Integrated confidence scoring and reinforcement‑learning feedback loops flag uncertain outputs for human review, maintaining high accuracy with low latency. APIs and BYOD connectors ingest data directly from S3, GCS, Azure, Minio, and other storage layers, enabling rapid, scalable document pipelines without custom development.
Target Audience
Primary users are data engineering, AI/ML, and software development teams at banks, insurers, mortgage servicers, and other enterprises that require high‑accuracy extraction from document‑heavy workflows.
Features
- Dual‑stream Vision‑Language Model that jointly processes visual and textual streams to extract tables, figures, and hierarchical structures with sub‑percent error rates.
- Domain‑aware Decoder that aligns extracted entities to custom ontologies, outputting clean JSON or Markdown for downstream LLMs.
- Hierarchical Indexing engine producing parent‑child chunk maps for fast semantic search and context‑preserving retrieval.
- Multi‑format ingestion pipeline supporting PDFs, PPTX, XLSX, DOCX, images, wikis, and database exports via BYOD connectors to S3, GCS, Azure Blob, Minio, etc.
- Confidence‑Score engine with RL‑based refinement, automatically flagging low‑confidence extractions for manual audit.
- Secure deployment options: on‑premise, air‑gapped, or cloud‑native, with end‑to‑end encryption, SOC 2 compliance, and strict access controls guaranteeing zero data leakage.
- Scalable RESTful API and SDKs for seamless integration into existing data lakes, warehouses, and AI/ML pipelines.
- Low‑latency processing architecture optimized for high‑throughput workloads, handling millions of pages per month.