01
Observe
Connect runtime behavior, task outcomes, and engineering context to understand the deployment as it is.
Intelligence, grounded in the physical world.
Critical Intelligence
Persistent deployment intelligence for Physical AI
From capability to reliability.Explore our approach
01 / The premisePhysical AI, in practice
Capability is a beginning.
Reliability is continuous.
A capable model still has to work in a particular place, on a particular machine, under conditions that keep changing.
Geometry varies. Calibration drifts. Materials, sensing, and operating conditions change. The distance between a successful demonstration and dependable operation is a deployment problem.
We are building Critical Intelligence to close that distance—and stay there as the world changes.
02 / The approachA persistent feedback loop
Stay connected.
Keep learning.
Runtime evidence meets engineering context. Every intervention is tested against the system it is meant to improve.
02
Understand
Identify emerging risk, changing operating conditions, and the gaps in what the system has learned.
03
Intervene
Prioritize the next useful experiment, calibration, adaptation, or operating change.
04
Verify
Measure the result on the physical system. Preserve what worked and continue observing.
Verified outcomes become deployment memory. The loop continues.
03 / The technical questionEvidence before iteration
What should the
system learn next?
Physical experimentation is expensive.
The next experiment should matter.
A targeted replay. A missing measurement. A calibration change. A carefully chosen physical trial.
Our approach uses existing data and simulation to identify where new evidence is most useful, then verifies the intervention on real hardware.
The objective is better real-world performance with less unnecessary physical iteration.
The principle
Observe continuously. Intervene deliberately. Verify in the real world.
04 / In the fieldBuilt around the deployment
One physical system.
A measurable next step.
We are validating the loop through controlled robotics experiments and field discovery with prospective deployment partners.
The starting point
A bounded reliability gap.
An instrumented system.
A result we can verify.
Our initial focus is engineered physical environments, where geometry, process requirements, and objective outcomes provide a foundation for rigorous validation.
We are interested in working with robot builders, autonomy teams, systems integrators, and operators who own the reliability loop.
Discuss a deployment05 / The teamResearch meets deployment
A technical problem.
A technical founding team.
Shahab Mousavi
AI, infrastructure, energy, and critical-material systems. Leads product direction, field development, and company strategy.
Amirhossein Afsharrad
Optimization, control, autonomous systems, and learning. Leads algorithms, architecture, experimentation, and technical validation.
06 / Start a conversationCritical Intelligence
The real world is
the next frontier.
Building or deploying a physical AI system?
We would like to understand what reliability means for you.