The agent storefront at https://agents.unsiloed.ai/llms.txt?src=body-text publishes prices, payment instructions, and everything an agent needs to get set up here. Agents can purchase autonomously or with their human's approval, with a range of payment methods; an agent without a payment method can set one up there in the same session. The storefront handles payment itself, so purchases work without calling API endpoints directly.
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.
“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.”
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.
Parse, 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.
#1 in terms of accuracy against the leading frontier labs,
IDP vendors, and open-source vision models.
Unsiloed Parser vs. leading document parsing platforms and OCR-capable VLMs
Source: olmocr==0.4.27 scorer (unmodified) on allenai/olmOCR-bench · Scores as of May 2026 · Higher is better
Read the full benchmarkLogos and trademarks are the property of their respective owners. Use does not imply endorsement.
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. 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; a layout stream captures image tokens, bounding boxes, alignment, and indentation hierarchy. A cross-attention layer fuses both, so the model reasons over content and structure together.
The decoder learns each domain's native ontology across legal contracts, financial reports, healthcare records, regulatory filings. Trained on millions of enterprise documents, not synthetic data. Outputs are schema-conditioned with cross-field constraints, so totals match line items and references resolve.
Bring a file your current tool mangles: a merged-cell table, a scanned filing, a mixed packet. Run it through Unsiloed and see typed, cited output in minutes.
Try it nowS3, 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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Tell us a little about your team, then pick a time.
Bring a sample document and we’ll show you structured output on it during the call.
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.
Head of AI
Fortune 150 Bank, NY
Page-based plans with included allowances and credit top-ups. Choose Free / Hobby, Standard, or custom Enterprise pricing.
For individuals and side projects trying out parsing.
For teams running parsing in production.
For high-volume, regulated, or self-hosted deployments.
Upload a sample PDF and get structured output in minutes.
No integration required.
