Dataset Versions
Create governed dataset versions as multimodal data evolves. Track changes, preserve dataset lineage, and keep every release auditable and production-ready.
Data Curation Platform
Multimodal Data Curation Platform for AI Teams
Curate multimodal training data with semantic search, embeddings, duplicate detection, dataset balancing, versioning, and connected annotation workflows in one platform.
No credit card required
DATASET CURATION & QUALITY CONTROLS
Curate focused, reliable training datasets before annotation.
Explore multimodal data, find relevant samples, inspect embeddings, remove duplicates and outliers, balance coverage, and save versioned subsets for annotation and model training.
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Semantic Search
Search image, video, audio, document, text, medical, and other multimodal data by meaning and similarity. Find relevant samples and rare cases without relying only on metadata.
Embedding View
Visualize multimodal dataset structure to identify outliers, near duplicates, labeling issues, and coverage gaps before annotation or model training.
Duplicate & Near-Duplicate Detection
Find repeated frames and visually similar samples before labeling. Keep the strongest representative, remove redundant work, and prevent teams from annotating the same content twice.
Metadata Filters & Saved Views
Combine source, environment, quality, annotation status, and model signals into reusable dataset views. Save focused subsets for assignment, review, export, and repeatable experiments.
Dataset Balancing
Measure class, condition, and scenario coverage, then build representative subsets that reduce dominant examples without losing rare, safety-critical cases.
Outlier & Quality Detection
Surface blurred, underexposed, corrupted, and domain-shifted samples before labeling. Review genuine anomalies, remove unusable data, and preserve valuable edge cases.
Why AI Teams Choose Unitlab for Data Curation
Turn raw multimodal data into focused, versioned, training-ready datasets without separating curation from annotation and quality review.
15X
Faster Dataset Preparation
90%
Less Manual Data Review
10X
Lower Curation Costs
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Questions, answered
Data Curation Platform FAQs
Answers about AI data curation, dataset preparation, semantic and similarity search, embeddings, duplicate detection, balancing, lineage, annotation workflows, and governed review.
Talk with the Unitlab teamWhat is data curation for machine learning?+
Data curation is the process of selecting, cleaning, organizing, enriching, and versioning raw data so it becomes useful for model training and evaluation. Unitlab keeps those decisions connected to annotation, review, and dataset release.
Dataset management documentationHow does semantic search help teams find useful data?+
Semantic search uses content meaning and similarity to surface relevant samples even when filenames and metadata are incomplete. Teams can discover rare cases, representative examples, and related media without inspecting every item manually.
Data exploration documentationCan Unitlab identify outliers and duplicate samples?+
Yes. Embedding and similarity views help teams inspect dataset structure, isolate outliers, and group duplicate or near-duplicate samples. Curators can retain representative items and reduce redundant labeling before training.
Dataset curation documentationHow do dataset versions and lineage work?+
Teams create controlled versions as data is filtered, annotated, reviewed, or released. Version history and lineage preserve which samples changed, why a subset was created, and which dataset supported a model or evaluation run.
Dataset version documentationCan curated data move directly into annotation workflows?+
Yes. Selected samples can move from curation into model-assisted annotation, human review, rework, approval, and release without exporting to a separate tool. This keeps assignments, issues, and quality decisions traceable.
Annotation workflow documentationWhich data modalities can Unitlab curate?
Unitlab supports image, video, audio, text, document, medical imaging, geospatial, and pathology data. Teams can use consistent dataset, ontology, workflow, and review controls across modalities.
Multimodal data documentationCan teams connect their own models to curation workflows?
Yes. Teams can connect custom models to generate predictions, embeddings, pre-labels, and quality signals. Human review remains part of the governed workflow before curated data is approved or released.
Model integration documentationDATA CURATION PLATFORM
Curate Training-Ready Datasets with Unitlab
Discover, filter, deduplicate, balance, version, and route multimodal training data into annotation and review from one governed workspace.
