01
Review layer included
Image annotation
Classification, bounding boxes, polygons, segmentation, keypoints, and attributes.
Images, video, and spatial data
Precise visual data for systems operating in the real world.
Annotation, validation, and edge-case discovery for image, video, geospatial, and sensor-fusion datasets.
Signal map
Program outcomes
Consistent taxonomies
Edge-case visibility
Production-ready datasets
Inside the work
Human expertise, connected to a controlled data workflow.
Every engagement links the people doing the work, the evidence used to review it, and the model behavior the program is meant to improve.
Capabilities
Built to fit the model, domain, and decisions behind your program.
02
Review layer included
Video annotation
Object tracking, event labeling, temporal segmentation, and activity recognition.
03
Review layer included
3D and sensor fusion
Point clouds, cuboids, trajectories, and synchronized multi-sensor review.
04
Review layer included
Visual evaluation
Human scoring for detection, grounding, generation, and multimodal reasoning.
What gets delivered
More than a dataset: a usable package of data, controls, and learning.
The exact artifacts change by program, but every delivery is designed to be inspectable, actionable, and ready for the next model decision.
01 / Design
Ontology package
Label definitions, examples, ambiguity rules, and a controlled change log.02 / Data
Validated annotations
Structured labels with review status and difficult-sample routing.03 / Insight
Coverage report
A clear view of edge cases, disagreement, and taxonomy performance.Delivery model
Designed for fast learning and controlled scale.
Programs move in visible stages, with a review point before scope, volume, or complexity increases.
- 01Frame
Design the ontology
- 02Calibrate
Calibrate against gold examples
- 03Produce
Annotate with layered quality controls
- 04Learn
Surface ambiguity and model-relevant edge cases