Perception and scenario data for systems that cannot fail quietly.

Autonomy programs are bottlenecked by long-tail scenarios and the cost of high-quality 3D and multi-sensor annotation. Teams need precise, auditable perception data and rare-event coverage that mirrors the real operational domain.

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AI use cases

Where it applies.

Perception model training
Behavior and trajectory prediction
Sensor fusion annotation
Edge-case and scenario mining
Safety case evidence
Regression evaluation

Data requirements

What it takes.

3D / LiDAR annotation
Multi-sensor synchronization
Long-tail scenario coverage
Track and instance consistency
Audit trails and versioning
Coverage tracking by ODD

Relevant data products

Products that map to this work.

Data Product

Physical AI & Robotics Data

Robot demonstrations, teleoperation, RGB-D, LiDAR, sensor fusion, and embodied trajectories.

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Data Product

Multimodal AI Data

Image-text data, video understanding, document AI, and cross-modal alignment at production quality.

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Data Product

Model Integrity & Evaluation

Private benchmarks, hallucination evaluation, safety red teaming, bias and compliance audits.

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Workflow

How the program runs.

01Scenario Scoping
02Taxonomy Design
03Annotation
04Multi-layer QA
05Evaluation
06Iteration

Continuous loop — outputs feed back into the data engine.

Quality & compliance

Built for regulated, high-stakes work.

Every engagement runs on our quality system and enterprise-grade security workflows — the controls an auditor would expect.

Audit-ready data lineage
Geographic data controls
Secure data handling
Versioned dataset releases

Quality & Security model

Case study

Proof in production.

Foundation Models

A Production Multimodal Pipeline for Document and Video Understanding

Image, video, document, and text annotations for multimodal model training and evaluation — one calibrated pipeline across four modalities with cross-modal QA.

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