Physics AI
- Physics-informed models
- Multi-modal sensor fusion
- Uncertainty quantification
- Simulation & digital twins
Physics AI × Physical AI
Where Physics AI
and Physical AI meet.
We fuse 2D and 3D sensing, natural language, and physical understanding so machines see reliably with the sensors you already have, and work alongside the people who run them.
- 2D + 3D in-situ data fusion
- Human-in-loop autonomy by design
- Reviewable AI you can certify
01 / The seam
Two worlds, one system.
Digital models only matter when they hold up on the factory floor. Physical systems only scale when the math behind them is trustworthy. phyXen works at the seam.
Physical AI
- Robots & manipulators
- Inspection cells
- Parts & assemblies
- 3D printers & process lines
02 / Focus
Three things we obsess over.
Multi-Modal
Cameras, depth, thermal, force, audio, logs. Real deployments rarely have the ideal sensor set. We get strong results from incomplete, affordable sensor combinations by fusing what is there.
- 2D / 3D in-situ analysis
- Works with imperfect sensor data
- Reasonable price, size, and complexity
Human-Physics
People and robots share the same physical world. We build systems that take instructions in plain language, understand CAD and paper procedures, and keep humans in the loop where judgement matters.
- Natural language to robot action
- Collaborative autonomy
- Instructions from drawings and documents
Reliability
Whatever sensor, model, or algorithm you use, the question is the same: how do you know you can trust it? We provide the review process and standard to answer that.
- Independent AI system review
- Technical, domain, and qual/cert lenses
- Repeatable evidence, not vibes
03 / Capabilities
Capabilities
The building blocks behind every phyXen engagement.
- 01
Multi-Modal Sensing
Fusing 2D imagery, 3D geometry, and process signals into one coherent picture of what is physically happening.
- 02
Human-Robot Interaction
Interfaces and control loops that let operators direct, correct, and trust robotic systems in real time.
- 03
Labeling
Domain-aware labeling pipelines for physical data, including 3D, multi-view, and time-series, so models learn from ground truth that is actually true.
- 04
NLP for Physical Systems
Language models grounded in physical context: turning specs, procedures, and spoken instructions into machine action.
- 05
System Autonomy with Collaborative Human Efforts
Autonomy that scales with confidence, escalating to people exactly when the system should not decide alone.
04 / Solutions
Solutions
Where we apply it today.
Multi-Modal Analysis
Combine the sensors you have into decisions you can act on, without waiting for the perfect sensor.
AI System Review
Independent review of AI systems across technical, domain, and qualification / certification dimensions.
Electronics Inspection
Multi-modal defect detection for boards, solder joints, and assemblies.
Welding Inspection
In-situ weld quality assessment from 2D, 3D, and process data.
Instructions to Robotic Systems
Tell the robot what to inspect or do in plain language; it goes to the right place and performs the task.
Commissioning Instructions
Guidance while the system is still being built, not just after it is finished. Consulting-style, context-aware.
Sustainment / Maintenance Chatbot
An assistant that knows your equipment, its history, and its manuals, and answers on the floor.
Workforce & Training
How-to guidance that turns tribal knowledge into repeatable, teachable procedures.
05 / Approach
How we work
- 1
Start from your sensors
We begin with the data and hardware already in place, and only add sensing when the math says it pays for itself.
- 2
Fuse, then reason
2D and 3D streams are fused into a physical model before any decision is made, so results hold up when one channel is noisy or missing.
- 3
Keep people in the loop
Operators direct systems in natural language and review edge cases. Autonomy grows as trust is earned.
- 4
Prove it
Every system ships with a review trail: what it sees, how it decides, and where it is uncertain. That is what makes it certifiable.
Contact
Building something physical?
Whether you have an inspection problem, a robot that needs to understand instructions, or an AI system that needs an independent review, we would like to hear about it.
Email us · contact@phyxen.ai