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NexaStack is the physical AI platform for running perception, decision and control models on machines, robots and plant floors. Every action an AI takes on a physical system is checked against safety boundaries first and recorded in a trace.
Edge, on-prem and sovereign deployment. Built by XenonStack.
Physical AI is artificial intelligence that perceives the physical world through sensors, reasons about what it sees, and acts on it through machines such as robots, vehicles, actuators and industrial controls. It combines computer vision, embodied AI models, simulation and edge computing so that decisions happen where the work happens.
A chatbot that gets an answer wrong costs a retry. A robot or a control loop that gets a decision wrong can damage equipment or hurt someone. That is why physical AI needs edge AI infrastructure, simulation and hard safety boundaries, not only a better model.
Vision models at the line catch defects in real time and route parts to rework, with every reject explained.
Coordinate mixed fleets of arms, AMRs and cobots: task allocation, handoffs and exception handling.
Vibration, thermal and current signals predict failures; agents raise work orders in your EAM or CMMS.
Picking, slotting, dock scheduling and yard visibility, connected to WMS and transport systems.
Asset inspection by drone and camera, load forecasting and grid-edge decisions with operator sign-off.
Buildings, campuses, ports and city systems: occupancy, safety monitoring and traffic response.
Decisions that need milliseconds run at the edge. Training, simulation and fleet oversight run in your cloud or data centre. NexaStack connects the two.
Optimised models on GPUs and accelerators beside the machine, working offline when the network drops.
Versioning, evaluation, drift detection and staged rollout of models to devices.
Provision, update and monitor thousands of edge nodes and robots from one control plane.
A live model of the plant or site, so AI decisions are tested against real state.
Train and validate policies in simulation, with synthetic data, before they touch hardware.
Sensor data joined with MES, ERP, maintenance history and shift rules, so a decision reflects what is actually happening on site.
Safety envelopes, interlocks and approval rules are checked before any command reaches a PLC or robot, alongside your existing functional-safety systems, never instead of them.
Every action is logged with the frame, the model version, the rule checked and the operator, ready for incident review and audit.
Physical AI earns autonomy one stage at a time: shadow (recommends only), supervised (operator confirms), bounded (acts inside a safety envelope), then full autonomy where the traces prove it is safe.
Operational and video data stays on your site or in your country. Run air-gapped, on-prem or in private cloud, with models you choose and control.
NVIDIA Jetson, Isaac and Omniverse, plus x86 and ARM edge servers.
ROS 2 and vendor SDKs for arms, AMRs and cobots.
Kubernetes at the edge (K3s and similar), containers and GitOps rollout.
OPC UA, MQTT, Modbus, and SCADA, MES and historian integrations.
Need agents for the business systems around the plant too? See XenonStack AI agent development services.
Nothing touches live equipment until it has passed simulation and shadow mode. Stage durations depend on site complexity and are agreed after assessment.
Pick the decision, map sensors, controls, safety rules and data.
Build the twin, train and test the policy against edge cases.
Install edge nodes, connect protocols, set safety boundaries.
Recommend beside operators; traces prove accuracy.
Raise autonomy where earned and roll out to more cells and sites.
| Dimension | Point AI solution | NexaStack physical AI platform |
|---|---|---|
| Scope | One camera, one model, one use case | Many use cases and sites on one platform |
| Deployment | Vendor cloud, manual device updates | Edge, on-prem or sovereign, with fleet rollout |
| Safety | Model confidence threshold | Safety boundaries checked before every command |
| Testing | Tried on the live line | Simulation and digital twin first, then shadow |
| Audit | Alert history | Decision trace: frame, model, rule and operator |
| Integration | Standalone dashboard | OPC UA, MQTT, MES, ERP and maintenance systems |
| Hardware | Locked to one vendor | Hardware- and model-agnostic |
One line or asset, one decision, simulated and run in shadow mode, with a go or no-go at the end. Scope and timeline are agreed per engagement.
NexaStack deployed across sites, with use cases added in waves and run under SLA by our engineers.
We build with your OT and data teams, train them, and hand over the platform to run in-house.
Embodied AI usually means a model that learns through a body, such as a robot. Physical AI is the broader category: any AI that senses and acts on the physical world, including robots, vehicles, machines and industrial controls.
Edge AI is where the model runs: close to the data, on local hardware. Physical AI is what the model does: act on the world. Most physical AI needs edge AI for speed, privacy and offline operation.
Edge inference, model ops, fleet management, digital twins and simulation, industrial connectivity, and safety controls with decision traces. NexaStack provides all of these in one platform.
It can be when it is introduced in stages. Policies are tested in simulation, run in shadow mode, and then act only inside safety boundaries, alongside your existing functional-safety systems.
Yes. Inference and safety checks run on edge nodes and keep working offline. Traces and model updates sync when the connection returns, or stay fully on-prem in air-gapped sites.
NVIDIA Jetson and other edge accelerators, standard edge servers, and robots via ROS 2 or vendor SDKs. Controls connect over OPC UA, MQTT and Modbus. NexaStack is hardware- and model-agnostic; certified device lists are confirmed per engagement.
With one high-value, low-risk decision, such as visual inspection or maintenance alerts, where AI recommends first and acts later. A single-cell pilot shows the value before any fleet rollout.
We'll pick the decision, map the sensors and controls it touches and set its safety boundaries, then show you what shadow mode would look like on your site.
AI continuously monitors systems for risks before they escalate. It correlates signals across logs, metrics, and traces. This ensures faster detection, fewer incidents, and stronger reliability
Proactive detection of performance and availability issues
Root-cause analysis across microservices and environments
Automated remediation playbooks to reduce MTTR
AI converts camera feeds into instant situational awareness. It detects unusual motion and unsafe behavior in real time. Long hours of video become searchable and summarized instantly
Real-time detection of suspicious motion or intrusion
Natural language video search and instant playback
Smart summaries for audits, investigations, and compliance
Your data stack becomes intelligent and conversational. Agents surface insights, detect anomalies, and explain trends. Move from dashboards to autonomous, always-on analytics
Connects to warehouses, lakes, and streaming sources
Question-answering in natural language
Continuous monitoring for anomalies and KPI deviations
Agents identify recurring failures and performance issues. They trigger workflows that resolve common problems automatically. Your infrastructure evolves into a self-healing environment
Automated diagnostics for recurring errors
Playbook execution: restart services, scale pods, clear queues
Feedback loop for improving remediation strategies
AI continuously checks controls and compliance posture. It detects misconfigurations and risks before they escalate. Evidence collection becomes automatic and audit-ready
Continuous control checks across infrastructure and SaaS
Automated evidence collection for audits
Risk scoring and prioritized remediation recommendations
Financial and procurement workflows become proactive and insight-driven. Agents monitor spend, vendors, and contracts in real time. Approvals and sourcing decisions become faster and smarter
Real-time visibility into spend and commitments
Anomaly detection on invoices and vendor performance
Intelligent workflows for approvals and sourcing decisions
