Turn raw data into production-ready training sets
PixlHub is a high-performance annotation workspace for AI teams that need clean datasets, precise ground truth, review workflows and operational visibility in one place.
Built around real data operations
PixlHub follows the same hierarchy your labeling work already needs: organizations, projects, assets and tasks. Assets are uploaded once, then reused across different project goals without duplicating data.
Organizations
Manage teams, workspaces, permissions and customer environments.
Projects
Define the ontology, task rules and annotation tools for each labeling goal.
Datasets & Assets
Store raw source files once and reuse them across multiple task pipelines.
Tasks
Turn assets into units of production with assignment, review and delivery status.
One interface for assets, annotation, QA and operations
Move from raw data to reviewed training sets without jumping between disconnected tools.
Precision annotation tools
Bounding boxes, polygons, points, freeform drawing and smart annotation tools support detailed visual labeling workflows.
Dataset operations
Track annotation history, split datasets into train/validation/test sets, import existing labels, and export in standard formats.
Review workflow
Managers can reject, comment, approve and monitor label quality before datasets move into training.
Private by default
Your data and models stay private. Enterprise teams can discuss custom storage, white labeling, and on-premise deployment.
Fast tools for dense annotation work
The annotation canvas is designed for speed: keyboard-first interactions, smart tool palettes and fluid geometry editing for complex frames.
From raw data to model-ready labels
Need the platform, the workforce, or both?
PixlHub can support internal labeling teams, while PixlData can also manage the annotation work end to end.