OCR gives you text. DocLoom gives you data.
UNRELIABLE
OCR breaks on complexity
Standard OCR breaks on complex layouts, tables, and low-quality scans.
DOCLOOM
Multi-engine routing always picks the best fit.
INCONSISTENT
Every tool, a different shape
Output format varies by provider - every tool produces different structure.
DOCLOOM
Markdown-first normalisation across all providers.
SILENT ERRORS
Bad fields slip through
Low-confidence extractions pass through without correction.
DOCLOOM
Confidence-aware self-correction on every low-quality field.
COSTLY
Cloud OCR adds up
Cloud OCR at scale adds up - especially for sensitive document types.
DOCLOOM
Local LLM option - zero marginal cost, zero data egress.
Upload once.
Get structured data back - clean, mapped, and verified.
1
Ingest
Upload PDF, JPEG, or PNG via API, SDK, or portal. Each document is analysed for layout, complexity, and Markdown compatibility before routing begins.
2
Route
The Decider Engine selects the optimal OCR provider by document type, layout, and cost — with a ranked fallback chain prepared if the primary provider fails.
3
Extract
Raw OCR output is normalised to clean Markdown — tables, headers, and structure preserved. An LLM maps it to your JSON schema using your custom extraction instructions.
4
Self-correct
Every field is confidence-scored. Fields below your threshold trigger automatic re-extraction with a higher-capability provider, then merge back into the final output.
Loops until verified
Live Demo
Live Demo
Watch DocLoom extract a real document — field by field.
Uploaded. Routed. Extracted. Self-corrected. Under 60 seconds.
Uploaded
Routed
Extracted
Self-corrected
Want to see it on your documents? Request a demo →
Four things no standard OCR tool does.
01
Markdown-first architecture
Every provider output is normalised into unified Markdown before extraction runs. LLMs process structured Markdown with significantly higher accuracy than raw text - fewer hallucinations on complex documents.
raw OCR text
# Markdown
LLM-ready
02
Confidence-aware self-correction
Set your confidence threshold. Any field below it triggers automatic re-extraction with a more capable provider - only that field, only that page. Corrected values merge back automatically.
3.96
0.62
Below 0.80 → re-extract automatically
03
Multi-provider routing with fallback
Azure Document Intelligence, AWS Textract, Google Document AI, and DotsOCR routes to the right engine per document - if the primary fails, the next in the ranked chain picks up automatically.
Azure AI
Textract
DotsOCR
04
Local LLM — DotsOCR
Run extraction inside your own infrastructure with DotsOCR, a locally hosted vision LLM tuned for complex layouts. Built for sensitive and regulated document environments.
Your perimeter
data-agree
Cloud, marketplace, or your own infrastructure.
DocLoom is available on the Azure Marketplace, directly as a SaaS, or deployed locally for environments where data never leaves your perimeter.
Extract structured data from any document - starting today.
Available on Azure Marketplace or deployed in your own environment. Python SDK and no-code portal both included.
Request a demo →
View on Azure Marketplace →
