Welcome to Unitlab

Understand Unitlab’s role as an enterprise multimodal data-production platform.

Unitlab brings durable data, annotation policy, human and model work, review, and reproducible delivery into one operating model. The platform is designed for teams that need to produce trustworthy multimodal datasets without losing the context in which a labeling decision was made.

Use this area when: you are evaluating the platform, onboarding a team, or deciding where a new production program belongs.

How this area fits into production

Unitlab projects overview in dark mode

Projects are the operating boundary that connects data, ontology, workflow, queues, annotation, review, and release delivery.

What this area controls

Unitlab connects four operating layers:

1. Data Space — ingest, connect, organize, filter, group, explore, and version raw or curated data.

2. Annotation — annotate images, video, audio, text, medical data, documents, and grouped multimodal cases.

3. Quality operations — define ontologies, route tasks through workflows, assign people, review outcomes, manage issues, and preserve instructions.

4. AI and automation — use interactive labeling assistance, tracking, model stages, external AI models, API keys, a Python SDK, and a CLI.

The platform is best understood as a lifecycle:

Raw files or cloud data
        ↓
Assets and folders
        ↓
Curation, filtering, embeddings, and multimodal grouping
        ↓
Versioned dataset
        ↓
Project + ontology + workflow
        ↓
Model assistance + human annotation + review
        ↓
Versioned release with annotations, files, metadata, and splits
        ↓
Training, evaluation, traceability, or another controlled project

This operating model matters because most training-data failures are not drawing-tool failures. They come from ambiguous label definitions, missing context, weak assignment rules, unreviewed model output, accidental dataset changes, and an inability to reproduce the exact data used by a model. Unitlab provides product surfaces for each part of that operating problem.

Workspace navigation

The Unitlab workspace is organized into:

• Annotation: Projects, Workflows, Ontologies

• Data Space: Assets, Datasets, Releases

• AI Suite: My AI Models, Public AI Models

• Workspace operations: Documentation/Instructions, Members, Settings, cloud storage, roles, and API keys

Each area uses the same core concepts—projects, data, ontologies, workflows, queues, review, and releases—so teams can standardize how training data moves from source to production-ready output.

Start with the right page

Decision	Production guidance
Orient a new user	Continue to Platform navigation.
Design a solution	Read the Unitlab object model before creating resources.
Prove the lifecycle	Run the end-to-end quickstart with representative data.
Approve scale	Use the production-readiness review after the pilot.

Operating boundary

• Data Space owns durable source resources and curation.

• Datasets own reusable, versioned membership.

• Projects own work, policy, routing, and quality state.

• Releases own frozen downstream delivery.

A production-ready handoff

A new user can name the current workspace, identify the correct starting area for their role, and explain how one source item becomes reviewed release output.

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Continue with Unitlab: cross-modal annotation workflows · Unitlab’s data annotation platform