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Xpulse
Turn machine and sensor data into degradation warnings, root-cause evidence, probabilistic RUL, maintenance actions and ROI intelligence.
Explore Xpulse →AithingX builds an industrial intelligence stack that helps heavy assets sense and predict, understand and simulate, then optimize and autonomize — while keeping engineers in control.
The platform progresses from equipment-level prediction to simulation, fleet reasoning and supervised autonomy. Each stage builds on the data, models and operational evidence created by the previous stage.
Turn machine and sensor data into degradation warnings, root-cause evidence, probabilistic RUL, maintenance actions and ROI intelligence.
Explore Xpulse →Fuse visual intelligence, machine physics, knowledge graphs and what-if simulation to explain what is happening and what to do next.
Explore Xsim →Scale from one machine to fleet co-optimization, GenAI copilots, supervised agents and progressive autonomous systems.
Explore NeuraX →AithingX is building an Industrial Physical Intelligence Platform for asset-intensive industries. We combine machine connectivity, sensor intelligence, Edge AI, cognitive digital twins and human-supervised agentic systems to help organizations understand physical assets and act with greater confidence.
Our platform starts with the data already generated by machines and selected sensors. It learns how an asset behaves, detects early degradation, explains likely faults, estimates remaining useful life and supports maintenance decisions with technical evidence.
From there, AithingX builds cognitive digital twins for simulation and what-if analysis, then extends intelligence across fleets through engineering copilots, supervised agents and progressive Physical AI. The aim is not to replace engineers, but to give them better evidence, faster insight and safer decision support.
Recommendations are grounded in machine signals, operating state, fault logic, uncertainty and asset context.
Maintenance and operational recommendations are connected to downtime, productivity, energy, cost and ROI.
Progress from prediction to simulation, agents and robotics while keeping validation, safety and human accountability central.
A practical deployment can begin with available machine signals and selected sensors, establish a trustworthy health baseline, then expand into digital-twin simulation and agentic workflows.