Capabilities
We work from data and hardware through model adaptation, integration, deployment, and field lifecycle.
We fit the workload to the device rather than forcing a cloud pattern onto the field. Compact language, vision, detection, segmentation, and anomaly models are paired with deterministic code and calibrated measurements.
Domain adaptation starts with a frozen input/output contract, representative data, and a baseline. We fine-tune compact multimodal models on controlled DGX-class infrastructure and promote only declared candidates.
Useful AI depends on traceable data. We design capture inventories, consent or usage-rights gates, expert worklists, repeat review, provenance, immutable dataset versions, and separation between development labels and product evidence.
Customer-controlled infrastructure can keep sensitive inputs and operational context close to the work. Connected, outbound-only, intermittently connected, and offline modes are designed as explicit operating states.
Cameras, lighting, touch terminals, physical controls, fixtures, serial links, radio, and environmental sensors become one tested product surface—not a pile of demos.
Remote autonomy starts with an energy ledger. We measure sleep, wake, sensing, radio, and image paths before sizing power; field nodes degrade deliberately while local gateways retain the heavier intelligence.
A model is only one release component. Applications, models, recipes, references, policies, and configuration travel with provenance, signatures, health checks, and a defined way back.
Model choice
Deterministic logic for explicit thresholds, state transitions, and safety boundaries.
Measurement, detection, segmentation, classification, and anomaly models for constrained inputs.
Compact language-and-vision models where context and structured synthesis add value.
Start with the constraint
We can design for a single appliance, a private site, an isolated fleet, or a battery-aware field network—then prove the smallest useful slice.
