A model that works in a demo still has to drive a real machine safely and on time. We connect inference to manipulators, sensors and controllers — the integration layer where most physical-AI projects succeed or fail.
Integration is where physical AI becomes real. The model is only part of the system — it has to read sensors, run on edge compute within a time budget, and command actuators safely and repeatably. Because Haink builds the software and supplies the hardware, the seam between the two is ours to own, not yours to manage.
Sensors and cameras to edge inference to controller commands — closed-loop, within the latency the task demands.
Robotic arms, grippers and motion controllers wired to model output, with calibration and motion planning.
Fail-safe behaviour, watchdogs and bounds on autonomous action — designed in, not bolted on — and built toward functional-safety standards (ISO 26262, IEC 61508) on IGX-class platforms.
Telemetry, monitoring and over-the-air model and config updates across one device or many.
For deployments where a machine can move near people, safety is not a feature added at the end — it is a design constraint from step one.
Builds that must be certified target ISO 26262 (automotive) and IEC 61508 (industrial) on NVIDIA IGX-class platforms with long, supported lifecycles.
Hard limits on speed, force and workspace, with watchdogs and fail-safe states, so the model can never command an unsafe action.
Timing, calibration and safety bounds validated on the target hardware before deployment — proven on the machine, not in a slide.
Start with one proven cell, then scale the proven cell — the expensive work is the pilot, not the next robot.
| Stage | What happens | Indicative cost |
|---|---|---|
| Pilot (one cell) | Scope, model, integration and safety proven on one machine | ~$50K–$150K data/training + hardware & integration |
| Scale to fleet | Proven policy replicated; mainly per-unit hardware and integration | Marginal cost per robot drops sharply |
| Operate | Fleet telemetry, monitoring and OTA model/config updates | Recurring — or bundled as RaaS |
Because the data and model work is largely a one-time cost, a single pilot amortizes across the fleet that follows. For operators who prefer to avoid upfront capital, robotics-as-a-service (RaaS) bundles hardware, software and support into a per-robot monthly fee. See the full physical AI deployment cost breakdown.
It connects the AI model to the machine: sensors and cameras for input, the edge compute that runs inference, and the controllers and actuators that execute motion — plus the safety, timing and software layers that make it reliable in production.
A model that works in a demo often fails at the seam between software and machine — timing, safety, sensor calibration, controller interfaces. One partner owning both the model and the hardware removes that seam and the finger-pointing between vendors.
A typical 2026 pilot budgets roughly $50K–$150K for the data and model-training stage, with edge hardware and integration on top; first pilots commonly land in the low-to-mid six figures, and scaling to a fleet drops the marginal cost per robot. See our deployment cost guide.
For deployments that require certification we target functional-safety standards ISO 26262 and IEC 61508 on IGX-class platforms, with bounded autonomy, watchdogs and fail-safe states designed in from the start.
Yes. Instead of upfront capital, hardware, software and support can be bundled into a per-robot monthly fee, which lowers the barrier to a first deployment and to scaling a fleet.
Both. We can integrate AI perception and control onto existing robots and controllers, or design a new cell from edge compute to actuator, supplying the hardware as part of the engagement.
Tell us the task — we’ll design the loop from sensor to actuator and supply the hardware to run it.