Description
Unitlab AI is a multimodal data platform built for AI teams that need to prepare production training data at scale. In one governed workspace, teams can curate data, run human and AI-assisted annotation, review and validate labels, and manage dataset versions, lineage and releases across many data modalities.
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The platform supports image, video, audio, text, PDF and document data, DICOM and volumetric medical imaging, whole-slide pathology, and geospatial imagery. Annotation is accelerated with AI-assisted pre-labeling, segmentation and object tracking, and teams can bring their own models to combine multiple prediction stages with human review. Ontologies, validation rules, queues, review stages and complete annotation history keep quality governed from initial labeling through dataset release.
Unitlab AI also includes embeddings, semantic search, similarity and curation tools, role-based collaboration and enterprise security. It offers cloud and on-premises deployment and connects to data, storage, models and workflows through an API, a Python SDK and a CLI. Plans range from a free-forever tier for individuals to Pro, per-seat and Enterprise options for larger teams.
- CategoryMachine Learning Platforms
- Pricing ModelFreemium
- DeploymentCloud, On-premises
- PlatformsWeb
Unitlab AI's Core Features
Multimodal data curation across image, video, audio, text, documents, DICOM, pathology and geospatial data
Human and AI-assisted annotation with pre-labeling, segmentation and object tracking
Bring-your-own-model workflows combined with human review
Dataset management, versioning, lineage and releases
Ontologies, validation rules, queues and governed review stages
Embeddings, semantic search, similarity and data curation
Role-based collaboration and enterprise security
Cloud and on-premises deployment with API, Python SDK and CLI
How to use Unitlab AI?
Connect your data: Bring data from cloud storage or other sources into a governed Unitlab workspace.
Curate and prepare: Search, filter, deduplicate and balance data, then route selected items into annotation.
Annotate with AI and humans: Use AI-assisted pre-labeling or your own models, then have annotators refine and edit predictions.
Review and validate: Run configurable review and approval stages with roles, issues, rework flows and validation rules.
Version and release: Version approved datasets with lineage and manage releases for downstream training.
Unitlab AI's Use Cases
- Computer vision datasets
- Medical imaging annotation
- Fintech document AI
- Multimodal NLP and audio
- Geospatial and aerial mapping
