Unsiloed AI

Unsiloed AI builds a “document layer” for enterprises that need their LLMs and AI agents to work with real-world documents. It processes PDFs, scanned images, and spreadsheets, turning them into structured Markdown or JSON so downstream systems can reason over content instead of treating documents as flat text.

It’s a fit if your team spends time on OCR, table parsing, and brittle parser pipelines, especially when layouts vary or documents change.

• Parse to LLM-ready Markdown. Converts documents into Markdown while preserving hierarchy (like sections) and structure (like tables, figures, and relationships that OCR often drops).
• Extract into JSON fields. Pulls specific fields into a single JSON output pattern, with domain-aware understanding (so values like totals and referenced items can stay consistent).
• Split for retrieval. Breaks multi-document files into individual documents and splits long files into retrievable chunks, keeping parent-child context together.
• Confidence scores per field. Returns confidence alongside extracted data so you can filter, review, or route low-confidence outputs.
• Private deployment options. Supports managed and air-gapped deployments, with the same API and output style.

What sets it apart is how it reads structure, not just pixels. The platform uses a proprietary dual-stream vision approach (one stream for content tokens and one for layout signals), then fuses them to reason over both what’s written and how it’s arranged. That focus on structure is the point, since it aims to keep tables as tables and keep document hierarchy intact for LLM and agent workflows.