Dynamic Layouts
Arrange related image, PDF, audio, and other files into resizable custom panels for each grouped workflow.
Annotate connected image, video, audio, text, document, and sensor data.
Arrange related image, PDF, audio, and other files into resizable custom panels for each grouped workflow.
Annotate images, video, audio, text, documents, medical, and other data in one unified environment.
Create precise image annotations for computer vision.
Accelerate image annotation with Magic Touch (SAM3), Find Similar Models, and AI-assisted tools for editable masks, bounding boxes, polygons, keypoints, and classifications.
Label and review 10,000+ tiny objects in a single image with responsive zoom, precise instance masks, and reliable performance at scale.
Track and label objects across video frames.
Play, pause, scrub, and step through video while audio waveforms, overlays, object tracks, and the timeline remain synchronized.
Select one or many objects, then track bidirectionally, forward, or backward from a trusted frame. Review propagated tracks and correct only the keyframes that need attention.
Annotate medical images and volumetric scans precisely.
Edit annotations at exact slices and review the same structure across anatomical planes with synchronized crosshairs and geometry.
Work across axial, coronal, sagittal, and 3D views while positioning, windowing, and annotation context stay synchronized.
Label whole-slide images from tissue to cellular detail.
Navigate seamlessly from whole-tissue overview to cellular-level detail across multiple resolution levels.
Handle gigapixel whole-slide images natively while preserving full-resolution pathology data.
Annotate large satellite and aerial imagery precisely.
Annotate massive satellite, aerial, and geospatial imagery without downscaling or splitting images manually.
Navigate seamlessly from large-area overviews to fine object-level details across multiple resolution levels.
Label entities, spans, relationships, and classifications.
Connect entities and spans to capture relationships, references, and structured associations.
Label people, organizations, locations, domain entities, and arbitrary spans for named entity recognition and information extraction.
Annotate speech, transcripts, speakers, sound events, and temporal ranges.
Select exact time ranges on the waveform, apply ontology-guided event classes, and review boundaries in synchronized audio context.
Play, pause, scrub, and zoom while the waveform, spectrogram, selected ranges, and playhead remain synchronized.
Annotate PDFs, text, tables, and page regions.
Annotate the original multipage PDF directly, without converting pages into image files. Navigate pages while preserving document identity, annotations, review state, history, and release context.
Select native PDF text, images, tables, and other embedded page content directly. Copy text normally or turn selected content into structured, page-aware annotations.
Search, version, and inspect multimodal datasets, connect AI models, and move annotations through review and approval.
Build workflows that connect models, annotation, review, and quality assurance in one continuous loop. Reduce handoffs and keep datasets moving from labeling to approval.
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world training datasets.
Create and manage dataset versions as data evolves. Track changes, assign work, and keep every release auditable and production-ready.
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Explore large multimodal datasets through semantic understanding instead of manual filters. Find relevant samples across diverse conditions.
Visualize dataset structure, identify outliers and labeling issues, and improve training-data quality before training.
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.
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