Edge first
Inference runs next to the machine, so decisions don't wait on the cloud. Models sit on the device or the plant server and act on the live signal.
NexaStack Super Intelligence
The senses and hands of enterprise super intelligence.
NexaStack brings governed AI agents into the physical world — factories, energy, logistics and infrastructure. Edge inference, agent orchestration and digital twins, with decision boundaries built for the moment AI starts moving atoms.
XenonStack runs in production on five-year contracts with a leading Indian bank and a Dubai government entity.
Safety boundary
Edge
agent Trust. bounded
agent Trust. bounded
01 Sense
Sensors
02 Perceive
Vision · signals
04 Act
Actuator
03 Approve
Human in the loop
Physical AI
Intelligence that perceives and acts in the real world.
Physical AI is AI that senses the physical world through cameras, sensors and machine signals, decides what to do, and acts through machines, robots or operators. Unlike chat or analytics, its outputs move equipment and materials, so it must run at the edge, in real time and inside strict safety boundaries.
Explore the platformBounded
Every agent has limits it cannot cross, whatever the model suggests. Speed, temperature, load and zone rules are enforced at runtime.
Human authority
Line stops, shutdowns and dispatches wait for an approver until trust is earned. Irreversible actions stay with a person.
Traced
What was sensed, what was decided, who approved and what happened is kept with every command, so the action can be reviewed after it moves equipment.
Platform
From sensor to decision to actuator, on one platform.
Run models and agents across edge devices, plants and private cloud, and manage the whole fleet from one place.
Edge inference
Agent orchestration
Vision AI
Digital twins
Edge & batch inference
Real-time inference on edge hardware, and batch inference for heavy workloads.
Agent orchestration & AgentOps
Coordinate agents across machines and sites, with versioning, rollout and rollback.
Model marketplace & LLM router
Pick vision, language and control models, and route each request to the right one.
Vision AI
Inspection, safety monitoring and counting from cameras already on site.
Digital twins & simulation
Test agents against a model of the plant before they touch the real one.
RL as a service
Reinforcement learning with evaluation and reward design, trained in simulation first.
Fleet management
Every device, model and agent in one console.
Safety
When AI moves atoms, boundaries are not optional.
NexaStack runs on the Elixir platform's Context, Control and Action, so every physical agent knows the situation, stays inside its envelope and leaves a trace.
Knows the plant
Asset hierarchy, maintenance history, shift and safety rules from ElixirData.
Safety envelopes
Hard limits on speed, temperature, load and zones, enforced at runtime. Irreversible actions need a human.
Traced to the actuator
Every command records the signal, the decision, the approver and the outcome.
Trust graduation on the shop floor
Promoted only when traces and simulations show readiness.
Shadow
Watches the line and proposes; operators act.
Supervised
Acts after an operator approves each command.
Bounded
Acts alone inside the envelope, escalates at the edges.
Full
End to end for proven tasks, with instant fallback.
Industries
Where physical decisions carry real stakes.
NexaStack is for plants, grids, yards, public infrastructure and inspection work, where a wrong action moves equipment, people or materials.
Explore all industries →
Manufacturing
Line-stop decisions, quality inspection and predictive maintenance run on the live signal from the line. A governed agent can flag a defect, recommend a stop, and wait for an approver before anything irreversible moves.
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Energy & utilities
Grid load balancing, asset health and field crew dispatch depend on what the plant and the network are doing right now. Agents watch load, equipment condition and open work, then propose the next action inside the limits operators already set.
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Transport & logistics
Yard and warehouse moves, route exceptions and fleet maintenance have to happen while goods are already in motion. An agent can spot a blocked dock, a late vehicle or a unit that needs service, and suggest the next move from the manuals and the live yard picture.
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Government & infrastructure
Emergency response, infrastructure inspection and public safety monitoring deal with sites, assets and people in the real world. Cameras and sensors feed an agent that can point to what changed, what needs a crew, and what must wait for a human decision.
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Insurance
Visual damage assessment, risk inspection and claims triage start from what a camera or a field visit actually shows. An agent can group the evidence, compare it with the policy rules, and prepare the next step for a reviewer.
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Blueprints
Start from a proven pattern.
Begin with a pattern already shaped for the edge — vision, an operator assistant, or asset health — then connect it to the sensors and systems you already run.
BP-01
Visuals agent
Camera-based inspection and monitoring.
View Blueprint → BP-02Operator assistants
Shop-floor copilots on manuals and procedures.
View Blueprint → BP-03AssetOps
Asset health, maintenance and work orders.
View Blueprint →
Runs with
NVIDIA Jetson
NVIDIA Isaac
NVIDIA Omniverse
ROS 2
Kubernetes at the edge
OPC UA
MQTT
Ecosystem
One part of a larger, governed super intelligence.
Super intelligence here means compound enterprise intelligence — not AGI. Each XenonStack product is one capability, sharing one context, one set of rules and one decision trace.
Memory & judgement
ElixirData
The shared context: what the plant is, what happened, and what the agent is allowed to use.
Immune system MetaSecureProtects models, tools and data so an agent cannot step outside its security boundary.
NexaStack
You are here
FAQ
Questions operators ask
Short answers on where physical AI runs, how unsafe actions are stopped, and how a first deployment starts.
Q1What is physical AI?
AI that senses the physical world, decides and acts through machines or operators — in real time, at the edge and inside safety limits.
Q2Does it need to connect to the cloud?
No. Inference and agents run on edge devices and on-prem servers; the cloud is optional for fleet management and training.
Q3How do you stop an agent doing something unsafe?
Safety envelopes are enforced at runtime, irreversible actions need human approval, and agents only gain autonomy through trust graduation.
Q4Will it work with our existing OT systems?
NexaStack connects over industrial protocols such as OPC UA and MQTT, and to MES, SCADA and historians.
Q5Where should we start?
Pick one line or site and one decision, run the agent in shadow mode against a digital twin, then graduate it.
Bring one physical decision under governed AI.
In an edge briefing we pick one line, site or fleet, map the decision to sensors, boundaries and actuators, and show what shadow mode would look like.