Intelligence,in motion.
Expert data. Model training.
Enterprise AI, from research to deployment.
See the work.
We turn expert knowledge into training data, model behaviour and reliable agents.
Precision-engineered models
for cutting-edge teams at
Pass it forward.
A demonstration becomes a training example.
RGB + pose + instructionPick the blue part.Observation → action → outcome
Expertise,
encoded.
Expert annotation. Embodied data. Independent review.
Specialist judgement across mathematics, science, code and physical systems.
Explore the method
Domain experts create, annotate and critique training material. Egocentric video and teleoperation capture physical work.
Model-assisted relabeling, deduplication and independent review produce structured corpora, preference pairs and task evidence.
Build the
foundation.
Examples become tokens. Predictions become learning signals.
Explore the method
Curated language, code and multimodal corpora pass through structuring, provenance, quality checks and deduplication.
The output: training-ready data and targeted evaluation sets.
Learn from
the feedback.
Predict. Measure error. Update the weights.
Explore the method
Supervised examples, human preferences, verifier evidence and recovery curricula shape model behaviour.
SFT, RLHF / DPO and targeted adaptation support task-specific behaviour, evaluated before deployment.
Models
meet tools.
Context. Execution. Evidence. Review.
Ground the task in repository context.
Intelligence
becomes control.
Observe. Act. Recover.
From demonstrations to policy
Teleoperation, egocentric video and simulation supply VLA task data. Policy classes include π0 and OpenVLA-OFT.
VLA SFT, recovery curricula, task coverage and closed-loop evaluation support physical deployment with safety interlocks and human overrides.
- Episode
- 33%
- X / mm
- −200
- Target / mm
- +1000
Tracking target1333.3 mm
v = clip(8e, ±1.4 m/s)
Private AI.
Governed by you.
Lightweight specialised instruct models.
3Bparameters in the model
Task specialisation · instruction tuning3 billion learned weights · 6 GB at BF16.
Inside governed deployment
Weight memory excludes KV cache, activations and runtime overhead. Parameter count alone does not measure quality.
Isolated workstreams, human approvals, red teaming, provenance and audit-ready delivery.
Onsite deployments follow client-defined residency and retention. Access controls, observability, human overrides and safety interlocks shape operations.
Scale the
whole system.
GPU compute. Fabric. Cooling. Checkpoints.
Infrastructure direction
Pre-training, post-training, simulation and inference need topology-aware scheduling, durable checkpoints, telemetry and recovery.
Our phased AI-ready data-center initiative begins with diligence and pilot capacity before a scaled campus.
The next
questions.
Research we’re following.