About
The data engine behind frontier AI.
We design, collect, curate, annotate, validate, and evaluate the data that production AI systems depend on — for foundation models, agents, multimodal systems, and embodied intelligence.
Mission
Make high-quality AI data an operating system, not a bottleneck.
The hardest part of building reliable AI is no longer compute — it's the data: what to collect, how to label it, how to verify it, and how to prove it improved the model. We exist to turn that work into a managed, auditable engine our customers can trust.
What we build
From alignment data to embodied sensor streams.
Six product lines spanning the full model lifecycle, delivered through one end-to-end data engine and one quality and security model.
Values
What we optimize for.
Quality over volume
A correct label beats a thousand noisy ones. Every program is built around a verifiable definition of done.
Expertise, not crowds
High-stakes data needs qualified, calibrated domain experts — and the systems to keep them honest.
Evidence over assertion
We measure model behavior and show our work: QA reports, agreement metrics, and audit trails.
Customer-owned outcomes
Your data, your rights, your model. We build the engine; you own what comes out of it.
Security by default
Isolation, de-identification, and governance are designed in — not bolted on after a questionnaire.
Continuous by design
A data program is a loop, not a delivery. Evaluation always feeds the next cycle.
By the numbers
One engine, measured end to end.
6
Product lines across the full model lifecycle
14+
Data modalities, from text to robotics trajectories
100%
Customer-owned training data and outputs
End-to-end
One data engine, one quality and security model
Leadership & advisors
Researchers, operators, and domain experts.
A team drawn from frontier AI, robotics, and regulated industries — building the systems and the standards behind every program.
Leadership
Frontier AI & data science
Operations
Managed data programs
Research
Evaluation & benchmarks
Advisors
Robotics & regulated industries
Why data quality matters
The model is only as good as the signal you teach it.
Once a model saturates public benchmarks, every further gain comes from the quality of supervision — expert reasoning, calibrated preferences, uncontaminated evaluation. That is the work we do, and the reason we measure everything.
- Verifiable definition of done on every program
- Inter-annotator agreement and consensus review
- Audit trails, lineage, and versioned delivery