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Unitlab AI

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Unitlab AI is an enterprise data annotation and curation platform for teams building computer vision and multimodal AI. It brings data selection, AI-assisted labeling, quality assurance and dataset management into one workspace, helping teams prepare reviewed training data with consistent standards and traceable decisions. Built for complex annotation workflows: - Video annotation: Track objects across frames, create temporal labels and review track continuity with frame-accurate controls. https://unitlab.ai/en/video-annotation - Medical image annotation: Annotate DICOM, CT and MRI studies with synchronized anatomical views, volumetric segmentation and expert review. https://unitlab.ai/en/medical-annotation - Pathology annotation: Label tissue regions, cells and nuclei in whole-slide images using multi-resolution viewing and structured pathology ontologies. https://unitlab.ai/en/pathology-annotation - Geospatial annotation: Annotate satellite and aerial imagery for buildings, roads and land cover while preserving spatial context. https://unitlab.ai/en/geospatial-annotation Curate relevant examples with semantic search, embedding views, metadata filters and duplicate detection. Use built-in AI assistance or connect your own models for pre-labeling, then route work through annotation, review, correction and approval. Quality controls include annotator consensus, hidden reference benchmarks, validation and issue resolution. Shared ontologies, role-based access and annotation history help teams coordinate work and trace quality decisions. Dataset versions preserve selected source-data membership, while annotation releases freeze reviewed project output for downstream training. APIs, a Python SDK and a CLI connect Unitlab with existing data pipelines. Hosted and on-premises deployment options support different infrastructure requirements. Start free or book a platform demo to evaluate Unitlab with your own data, labeling requirements and review workflow.

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