76k+ open-source stars, hardened in production.
Marker, Surya, Chandra, and Indra are ours, and the same engines run inside the commercial platform.
FOR AI RESEARCH LABS
Document parsing for the labs building frontier models.
Leading research labs rely on Datalab to accurately turn messy PDFs and other documents into clean, reliable training data. The same open-source models (Marker, Surya, Chandra), hardened, customized, and supported by the team that builds them.
TRUSTED BY
We can handle any document challenge.
D · 01 arXiv preprints
Multi-column, equations, citations, and figures preserved
Try in playground →D · 02 Conference proceedingsLayout-aware, multi-column, multi-figure
Try in playground →D · 03 Lab notebooksMixed handwriting, tables, code blocks
Try in playground →D · 04 PatentsClaims, drawings, and structured metadata
Try in playground →D · 05 Web crawlsHTML to clean training markdown, 90+ languages, at scale
Try in playground →D · 06 Internal RFCsOn-prem parsing of unpublished research
Try in playground →Open models, published numbers, and the team that trains them.
Published benchmarks against named competitors.
94.1 on OmniParseBench and 86.9 on olmOCR-bench, with every test, competitor and verdict published in full.
Burst to 1B+ pages in a day on our GPUs.
Our managed batch system scales to throughput-optimized workloads, with capacity for more than 1B pages in a day.
Models tuned to your document edge-cases.
We train our own models, and can adapt or make new ones tailored to your document processing needs.
Run it in our cloud, your VPC, or fully air-gapped.
- Managed cloud
Start with an API key — nothing to host or operate.
Sign up → - EU data residency
Run in-region, with no egress to US infrastructure.
Sign up → - Your VPC
Runs inside your own AWS, GCP, or Azure account.
Talk to sales → - On-prem & air-gapped
Fully offline, the same model weights, dedicated support.
Talk to sales →
SOC 2 Type II · BAA available View our trust center →
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