Unified Labeling Interface for Multimodal Data Annotation
Annotate multiple data types together in a single labeling interface, with shared context, synchronized views, and consistent annotation workflows.
Multimodal Annotation Platform
Multimodal Annotation Platform for AI Training Data
Annotate connected image, video, audio, text, document, and sensor data in one governed workspace with shared ontologies, relations, and review workflows.
No credit card required
Multimodal annotation features
Everything You Need for Complex Multimodal Annotation
Organize related image, video, audio, text, and document files into one governed case using Data Groups, filename patterns, and custom layouts.
Dynamic Layouts
Arrange related image, PDF, audio, and other files into resizable custom panels for each grouped workflow.
Advanced Ontologies
Define shared classes, nested attributes, and structured metadata consistently across modalities.
Auto-Grouping
Automatically group related multimodal data into unified annotation tasks for synchronized labeling and review.
Multi-Camera Data4 angle view
Multi-Camera Annotation
Annotate four camera views of one robotics task to follow the robot, gripper, cartons, and pallet across the work cell.
Four camera viewsShared task context
Cross-Modal Annotation
Label and connect information across different modalities within the same dataset or task.
Built for AI Data at Scale
Annotate complex multimodal datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
15X
Faster multimodal data annotation
Unified labeling tools and automated workflows reduce manual multimodal annotation.
90%
Automated multimodal annotation
On average, 90% of multimodal labels are pre-labeled automatically, then reviewed and refined by humans.
10X
Lower Training Data Costs
The cost per accepted label can be up to 10× lower as curation, annotation, and QA are automated.
Supported multimodal annotation types
All multimodal annotation types in one platform.
Use compatible native annotation types across grouped image, video, audio, text, PDF, and medical viewers, plus Item Properties and Relations at case level.
01
Bounding box
Mark rectangular regions in supported visual viewers; tracking applies only to compatible video sequences.
02
Segmentation
Create pixel-accurate masks in compatible image, video, or medical viewers.
03
Polygon
Outline irregular regions in compatible visual viewers, including document markup when supported.
04
Audio event / range
Label sound events or time ranges, segment speakers, and transcribe speech in the native audio viewer.
05
Line / polyline
Trace paths, edges, and document markup in compatible native viewers.
06
Entity / span
Mark entities and spans, then connect them with ontology-backed relations in the native text viewer.
07
Native text selection
Select document text natively and add compatible visual markup when PDF review requires spatial context.
08
Item Properties
Label source, severity, quality, and other properties that describe the grouped case.
09
Relations
Connect evidence, entities, and compatible annotations across the case using explicit relations.
Multimodal annotation quality assurance
Build quality into connected multimodal annotations.
Compare labels across related sources, check approved references, and resolve issues with each native view in context. Keep quality decisions connected to the correct items, properties, and relationships.
Multimodal Annotation Consensus
Compare independent annotations within the same grouped item. Review disagreements in supported labels, source properties, and relationship endpoints across native views.
Connected source evidenceAgreement checksReview disagreements
Quality Gate (Honeypot)
Check supported annotations against approved references hidden from annotators. Evaluate the relevant source panels and preserve not-evaluated outcomes when evidence cannot be compared.
Validation & Issue Resolution
Inspect required properties and relationship issues across connected sources. Correct the affected annotations in their native views and resolve feedback before group approval.
Multimodal QA Analytics
Track benchmark results, consensus outcomes, and review decisions for grouped tasks. Inspect the relevant source evidence when deciding which items need further review.
Benchmark resultsReview outcomes
Multimodal dataset curation & annotation workflows
Curate multimodal data and automate annotation workflows.
Search, version, and inspect multimodal datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as multimodal data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Search mixed-source cases and return consistent image, document, audio, text, or video evidence for one query.
Embedding View
Inspect supported case- or asset-level clusters, similar samples, outliers, and quality issues before training.
Integrated Workflows
Route a grouped task through native-view annotation, case review, quality assurance, rework, and release while preserving context.
Bring Your Multimodal Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world multimodal datasets.
Solutions
Multimodal annotation solutions for connected AI data
Build reviewed datasets that bring related views, recordings, documents, and instructions into one labeling workflow.
Visual Quality Inspection
Review inspection images alongside related process recordings and notes. Label visible defects for consistent visual inspection training data.
Explore visual quality inspection →
Robot Demonstration Datasets
Label recorded robot tasks with multiple camera views, object annotations and instruction context for embodied AI datasets.
Explore robot demonstration datasets →
Audio-Visual Sound Localization
Mark audible events and their visible sources in related recordings to prepare reviewed audio-visual training datasets.
Explore sound localization →
Document Visual Question Answering
Prepare document question-answer pairs with source evidence, explicit answer fields and human review for document understanding models.
Explore document visual question answering →Questions, answered
Multimodal Annotation Platform FAQs
Answers about grouping related files, native viewers, shared ontologies, review, curation, and model-assisted workflows.
Talk with the Unitlab teamWhat multimodal annotation types does Unitlab support?+
Use boxes, masks, polygons, and lines in visual viewers; audio events, ranges, segmentation, and transcription in audio; entities, spans, and relations in text; native text selection and compatible visual markup in PDF; plus Item Properties and Relations for grouped-case context.
Annotation documentationHow do Data Groups keep related multimodal files together?+
Filename patterns can group related uploads into a Data Group. The group becomes one assignable case, and a configurable layout presents each file through its compatible native viewer.
Multimodal annotation documentationHow do active and passive panels work in a multimodal layout?+
One active panel hosts the compatible native editor for the selected file. Passive panels preserve supported context from the same case without originating annotations or edits.
Multimodal layout documentationHow do shared ontologies and case properties work?+
A shared project ontology defines classes, classifications, properties, and supported relations. Item Properties can capture case-level context while file annotations remain compatible with each native viewer.
Properties and relations documentationHow does Unitlab manage multimodal annotation quality and review?+
Configurable workflows route multimodal tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for multimodal pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version multimodal datasets in one platform?
Yes. Teams can curate multimodal data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationMULTIMODAL ANNOTATION PLATFORM
Build Production-Ready Multimodal Datasets with Unitlab
Curate connected data, annotate each file in its native viewer, and review the complete case. Keep shared context, ontology rules, and reviewed annotation releases connected throughout your multimodal workflow.
