Datalab

Datalab provides document intelligence APIs to convert PDFs, spreadsheets, images, and other formats into structured, machine-readable outputs. The platform supports managed cloud, on-premises, and open-source options, and is used to feed AI systems, build pipelines, and automate workflows with auditability and data provenance. Startups, enterprises, and research teams use Datalab to transform challenging documents into usable data without sacrificing security or transparency.

Datalab emphasizes conversion accuracy, openness, and security. It presents a pipeline architecture where processors can be composed, deployed, and monitored, enabling continuous improvement and governance across document processing tasks.

In its platform, Datalab highlights processors such as Convert, Extract, Segment, and Eval, each with specific capabilities for transforming documents, extracting fields with citations, identifying document boundaries, and evaluating output quality. The tool supports multiple deployment modes, including managed cloud, VPC deployment, and on-premises/offline options, with SOC 2 Type II and data residency considerations.

Datalab describes pricing with three tiers: Free, Team, and Enterprise Custom, including a free monthly allowance on the hosted API and pay-as-you-go rates. The platform supports running in your own VPC or fully air-gapped deployments, and offers migration credits and startup programs. Documentation notes integration with APIs, SDKs, and open-source models such as Marker, Surya, Chandra, and Indra.

Use cases

• Training corpora and data for AI research and lab workflows