Annotate image, video, audio, text, documents, DICOM and volumetric medical data, whole-slide pathology, and geospatial imagery in one platform. Unitlab AI keeps multimodal training data consistent with shared ontologies, AI assistance, and governed quality control.








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Curate, organize, search, and version multimodal datasets in one place. Inspect image, video, audio, text, document, medical, pathology, and geospatial data while preserving lineage across annotation and review workflows.

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Unitlab AI is an enterprise multimodal data platform for production AI. Teams use one governed workspace to curate, annotate, review, version, and manage training data across image, video, audio, text, documents, medical imaging, pathology, and geospatial data.
Unitlab supports image, video, audio, text, PDF and document data, DICOM and volumetric medical imaging, whole-slide pathology, and geospatial imagery. Connected data can also be organized and reviewed through multimodal annotation workflows.
Teams can discover and prepare data, route selected items into annotation and review, then version and manage approved datasets without moving between disconnected tools. Ontologies, properties, lineage, and access controls remain consistent across the workflow.
Unitlab supports AI-assisted pre-labeling, segmentation, object tracking, and repetitive labeling. Teams can also bring their own models, combine multiple model stages with human review, and edit or validate every prediction before approval.
Unitlab supports configurable review and approval stages, role-based assignments, issue and rework flows, validation rules, and complete annotation history. This gives teams governed quality assurance from initial labeling through dataset release.
Yes. Unitlab is built for large multimodal datasets and demanding formats, including long video and audio, volumetric DICOM studies, whole-slide pathology, large geospatial imagery, and multi-page documents. Dataset versions, queues, and workflows keep long-running projects organized.
Yes. Unitlab supports hosted and on-premises deployment options. On-premises deployments keep data within your controlled infrastructure, while hosted deployments use encrypted, isolated storage and role-based access controls. Contact Unitlab to review the deployment model that fits your security requirements.
Yes. Teams can connect data, cloud storage, models, and workflows through the Unitlab API, Python SDK, and CLI. Unitlab also integrates with common cloud and machine learning infrastructure so existing pipelines can remain in place.
Yes. You can start with Unitlab AI for free without a credit card. Paid plans are available for larger teams and datasets, advanced collaboration, security, storage, deployment, and workflow requirements.
See how teams use Unitlab to annotate data, collaborate, and scale AI workflows.














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