We turn PDFs, images, and spreadsheets into JSON and Markdown your LLMs and AI agents can reason over. Built on our proprietary dual-stream vision models.
of enterprise data is unstructured. Most of it sits in PDFs, scans, and spreadsheets your LLMs can't read.
AI teams spend stitching parsers, OCR, and post-processing. Pipelines break the moment a layout changes.
of in-house document pipelines reach production. The rest stall in the pilot.
of enterprise data is unstructured. Most of it sits in PDFs, scans, and spreadsheets your LLMs can't read.
AI teams spend stitching parsers, OCR, and post-processing. Pipelines break the moment a layout changes.
of in-house document pipelines reach production. The rest stall in the pilot.
LLMs exploded the use cases for unstructured data. Agents underwrite claims, copilots draft credit memos, RAG retrieves across thousand-page filings. Generic OCR and DIY pipelines are too static. You need a more dynamic interface, so we rebuilt the stack with vision models that read documents the way humans do.
Learn moreParse, extract, and split. Use them standalone or chain them end-to-end. The same API runs a quick prototype and a production pipeline at scale.
The quarterly report highlights consistent growth across all divisions...
Convert PDFs, scans, and images into LLM-ready Markdown. Vision models read text, tables, figures, and hierarchy in a single pass, preserving structure that OCR loses.
"vendor": "Apex Industries",
"date": "2024-02-14",
"amount": 4200,
"currency": "USD"
"invoice_id": "INV-0042"
"payment_terms": "NET_30",
"confidence": 0.98
Pull the fields you need into JSON. One schema, every layout, with domain-awareness that knows a freight charge isn't a line item.
Break multi-document files into individual docs and long ones into retrievable chunks. Parent-child indexing keeps clauses with their preambles.
Reads pages like a human, breaking them into typed regions: tables, figures, signatures, handwriting. Attention-guided heatmaps focus compute on pivot zones: numerical columns, merged cells, section headers. Chunking respects these pivots, so multi-page tables stay whole, split rows rejoin, and clauses keep their hierarchy.
Two streams process the document in parallel. A data stream captures tokens, numbers, and entities, while a layout stream captures image tokens, bounding boxes, alignment, and indentation hierarchy. A cross-attention layer lets the model reason over content and structure together.
The decoder learns each domain's native ontology, whether it's a legal contract, a financial report, a healthcare record, or a regulatory filing. We trained it on millions of real enterprise documents across these domains, not just synthetic data. Outputs are schema-conditioned with cross-field constraints, so totals match line items and references resolve.
S3, SharePoint, Drive, Snowflake, your DMS. We sit on top of where your data already lives.
Configure schemas, prompts, and confidence thresholds. Or chain all three.
[Unsiloed AI.]
Parsing data...
JSON, Markdown, or structured fields into your LLM, AI Agents, vector DB, or warehouse.
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The accuracy, especially on tables, is meaningfully better than anything we tested. We evaluated over 15 solutions and Unsiloed was the only one that worked reliably.
Unsiloed handled edge cases that consistently broke other systems, particularly the ones around nested tables and different formats.
Mortgage servicing documents are extremely complex, but Unsiloed was the only system that parsed them with high accuracy. Confidence scoring and flagging reduced hours of manual review for our team.
Unsiloed processes a wide range of formats including PDFs, images, spreadsheets, and scanned documents. It can handle mixed layouts such as tables, charts, forms, and handwritten content within a single file.
Traditional OCR returns a flat stream of text. Unsiloed uses vision models that understand structure — tables stay as tables, sections stay grouped, and the output preserves the parent-child relationships your downstream LLMs need.
Clean, LLM-ready Markdown and structured JSON, with confidence scores per field. Fully schema-validated outputs are available for extraction tasks where you need a guaranteed shape.
97.4% table extraction accuracy on our internal benchmark vs ~61% for industry alternatives. Accuracy scales with the complexity of the schema and is reported per-field via confidence scores.
Yes. We support both managed and air-gapped deployments. The same API and outputs work in either mode, so the integration code stays identical.
Usage-based for managed deployments and seat-based for self-hosted. Talk to us about volume — we have custom pricing for high-volume document pipelines.
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