Unitlab AI

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.

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.

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?

1. 1

   Connect your data: Bring data from cloud storage or other sources into a governed Unitlab workspace.

2. 2

   Curate and prepare: Search, filter, deduplicate and balance data, then route selected items into annotation.

3. 3

   Annotate with AI and humans: Use AI-assisted pre-labeling or your own models, then have annotators refine and edit predictions.

4. 4

   Review and validate: Run configurable review and approval stages with roles, issues, rework flows and validation rules.

5. 5

   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

FAQ from Unitlab AI

• What is Unitlab AI?

  Unitlab AI is an enterprise multimodal data platform where teams curate, annotate, review, version and manage training data in one governed workspace across many data types.

• What data modalities does it support?

  It supports image, video, audio, text, PDF and document data, DICOM and volumetric medical imaging, whole-slide pathology, and geospatial imagery.

• How does it accelerate annotation?

  It supports AI-assisted pre-labeling, segmentation and object tracking, and teams can bring their own models and combine multiple model stages with human review.

• How is annotation quality managed?

  Configurable review and approval stages, role-based assignments, issue and rework flows, validation rules and complete annotation history provide governed quality assurance.

• Can it run on premises?

  Yes. Unitlab supports both hosted and on-premises deployment, with on-premises keeping data inside your controlled infrastructure.

• Does it offer APIs and SDKs?

  Yes. Teams can connect data, storage, models and workflows through the Unitlab API, Python SDK and CLI, and it integrates with common cloud and ML infrastructure.