Forward-deployed AI engineers. One practice, many applications.
We embed a senior AI-native pod inside your organization and own delivery from roadmap to production. Our current flagship practice — and the rest of this page — is high-fidelity multimodal datasets for embodied AI and humanoid robotics. The same pod model ships eight other AI patterns; see use cases and how we work.
- AI Pre-Labeling Accuracy
- ≥ 99.2%
- Video ↔ State Sync Latency
- ≤ 50 ms
- Format Export
- LeRobot · HDF5 · RLDS
Production-grade data sourcing. No hardware overhead.
We act as your primary data engineer and general contractor. We design the trajectory data models, coordinate frames, and sensory states based on your target robot kinematics, then dispatch collection to our verified network of specialized teleoperation factories and sensor-fusion testbeds across the CEE / CIS region.
Data Architecture First
High-frequency recording of RGB-D camera feeds, robot joint states (proprioception), and force-torque (F/T) telemetry — designed against your target robot kinematics before a single trajectory is collected.
Distributed Lab Network
We absorb the hardware, facility, and staffing overhead across a verified network of teleoperation factories and sensor-fusion testbeds. Your engineering team stays remote; we manage the physical interaction loops.
Western-Compliant Delivery
Raw stream cleanup, multi-camera timecode syncing, and automated data packaging — securely delivered straight to your AWS or GCP buckets. EU contracting entity, DPA-ready.
High-fidelity interaction scenarios.
Edge-Case Recovery (RaaS)
Datasets engineered with a deliberate 5–15% fraction of structured failure modes — slips, missed clips, object drops — followed by human-guided successful recoveries. Increases live deployment model resilience by ~15%.
Deformable Objects & Liquids
High-density interaction sequences with complex fluids, fabrics, clothing, soft plastics, and transparent or highly reflective labware.
Bimanual Manipulation
Synchronized dual-arm workflows, precision pipetting, machine tending, and SKU-handling sequences mapping complex physical contact dynamics.
Transparent pricing. No hidden friction.
Custom Teleoperation
- Full-body or dual-arm custom testbed environment setup
- Multi-view RGB-D tracking + synced proprioception logs
- AI-assisted dataset validation and cleaning
- Full export to LeRobot v3 / Open X-Embodiment pipelines
Domain Bundles
- Baseline interaction data (Kitchen-100, Lab-100, or Logistics-100)
- Instant digital delivery of standard movement trajectories
- Comprehensive environment variations and object tracking
- Optimized for immediate foundation model pre-training
Standard environment props included. Custom, specialized hardware components or highly unique physical retail / industrial SKUs must be provided by the client, or are billed separately under explicit milestone scopes.
Datasets are one application. The model is the same for every engagement.
A senior AI-native pod, embedded inside your org, owning delivery from roadmap to production. We chose dataset engineering for embodied AI as the lead practice because it stresses every part of the model — spec, infra, data, evaluation, hand-off — and because the market is short on operators who can run it end-to-end. The same pod ships the other patterns below.
Engineers inside your VPC, repos, ticketing and standups. Not a delivery center behind a PM.
We design the workflow, data layer and human-in-the-loop assuming AI is already part of the system.
A pod of 3–6 senior engineers assembled around your stack. Roles fluid, not per CV.
The pod owns production deployment, adoption metrics and rollback. Not tickets closed.
Same model, different domain.
Not every reader is here for robotics datasets. The pod model below is the same one that produces them — pointed at a different problem.
Enterprise AI use cases
Sales ops, SOC alert triage, contract search, internal copilots, RAG over private docs — the patterns we ship most often outside robotics.
Manufacturing & industrial
Predictive maintenance, vision QA, supply-chain GenAI. Documented deployments and a calibrated ROI calculator.
Managed AI services
Once a system is in production, the same pod can run evals, drift checks, and model updates on an ongoing basis.