01
Review layer included
Sensor annotation
Camera, LiDAR, radar, and synchronized sensor-fusion labeling.
Perception and prediction
High-fidelity data for systems navigating complex environments.
Visual and sensor-data workflows for perception, mapping, planning, and safety-critical edge-case discovery.
Signal map
Program outcomes
Precise spatial labels
Consistent temporal tracks
Focused edge-case coverage
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
Scene understanding
Objects, lanes, zones, events, intent, and relationships across time.
03
Review layer included
Quality diagnostics
Agreement analysis, taxonomy audits, and difficult-sample review.
04
Review layer included
Scenario evaluation
Structured testing around rare, ambiguous, and high-consequence conditions.
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
Define operational scenarios
- 02Calibrate
Build geometry-aware instructions
- 03Produce
Run staged annotation and QA
- 04Learn
Feed difficult cases back into development