Your AI will break.
It must never break silently.
PhysOpsAI is a verification-and-assurance R&D company for AI in the physical world. We build the instruments and the mathematics that turn a system's reliability into a checkable property.
The discipline is version-bound evidence: what exactly was tested, which claim that evidence supports, what went stale after a change, and what must be rerun before deployment or return to service.
Verification is where the value migrates.
Machine cognition is commoditizing. As outputs become cheap, value moves to whatever converts an output into a consequence that can be trusted. Someone has to own the layer that checks.
In the physical world, failure is quiet. A model that was right yesterday meets new hardware, a thermal limit, a changed environment, a silent update, and its behavior shifts with no error message. Software fails loudly; deployed intelligence degrades in silence.
So the unit of trust is an evidence package bound to a version: this system, this model, this data, this environment, these assumptions, this claim. Change any element and part of the evidence expires. Knowing which part, and exactly what must be rerun, is the discipline we are building.
Bounded Claims
Every result names the exact configuration and the exact claim it supports, with its assumptions stated.
No universal safety promises. Those are how trust gets broken.
Evidence Expires
Material change invalidates evidence selectively. The discipline is knowing what expired and what survives.
Rerun what must be rerun. Nothing more, nothing less.
Seeing Comes First
If you cannot observe a system's true state, you cannot verify it. Instrumentation precedes assurance.
That is why our first product is an instrument.
SystemPulse
Direct Distribution · 2026SystemPulse is a real-time system monitor for Apple Silicon Macs. It surfaces the power, thermal, and performance truth the operating system does not expose: per-domain power draw, thermal headroom with throttle prediction, per-process GPU utilization, and inference profiling built for local AI workloads.
It exists because observability precedes verification. Before you can trust a machine that thinks, you must see what it is actually doing, watt by watt, degree by degree.
No App Store. No subscription. No tracking.
Download opens at release.
We publish what we can prove.
The research program behind PhysOpsAI asks when a learned system's good behavior survives deployment. It treats robustness, generalization, and interpretability as one geometric question rather than three separate patches.
Research papers are under peer review in 2026. Results become public as they are accepted; mechanisms stay protected until they are filed. That order is deliberate.
US provisional patent filed 2026 · Patent Pending
M.R. Amiri
Founder, CEO & Chief AI Scientist
M.S. Computer Science (in progress), Georgia Institute of Technology
Specialization: AI & Computing Systems
Bachelor's Degree in Computer Science & Data Science, New York University
The Courant Institute School of Mathematics, Computing, and Data Science
Research in geometric representation learning: when neural networks learn invariant structure instead of environment-specific shortcuts, and how that property can be measured, enforced, and verified.
Graduate coursework at Georgia Tech runs across the machine-learning systems, network security, health-informatics, and cyber-physical stack the work draws on: under Prof. Wenke Lee (Network Security), Prof. Jimeng Sun (Big Data for Health Informatics), Prof. Jon Duke (Health Informatics, a founding member of the OHDSI consortium), and Prof. Saman Zonouz (Cybersecurity of Drones), a PECASE recipient who leads Georgia Tech’s Cyber-Physical Security Laboratory. The representation-learning foundation is from NYU, under Prof. Yann LeCun (Deep Learning, audit) and Prof. Alfredo Canziani (Intro to Deep Learning).
That range, from how models learn to how deployed systems degrade quietly in the field, is the ground PhysOpsAI's verification and assurance work stands on. USPTO patent bar candidate; US provisional patent filed 2026. Three entities founded across research, product, and consulting.
PhysOpsAI Inc. · Orlando, Florida
Building physical AI that has to be trusted?
The right first conversation is concrete: a system, a failure mode, or a claim worth checking.