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.
Embed AI agents into your existing systems
Seamlessly integrate AI agents into your current platforms to elevate capabilities, optimize workflows, and accelerate innovation.
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 →
