INDUSTRY
Real-time perception, sensor fusion, and on-device models for machines that move — not in the cloud.
THE CHALLENGE
A robot can't wait for a cloud round-trip; perception has to keep pace with motion.
On battery-powered platforms, silicon efficiency translates directly into operating hours.
Physical AI is moving to transformers and vision-language-action models that older edge chips run poorly.
USE CASES
Multi-camera perception and on-device models for balance, navigation, and manipulation.
Navigation, mapping, and obstacle detection in warehouses and factories.
Perception and inspection on board, where weight and power are the tightest.
Grasp planning at line speed, and human detection for safe motion alongside people.
WHERE PATHINAI FITS
Workload Intelligence
Captures the real sensor-to-actuator latency chain across cameras, IMU, and lidar.
Virtual Silicon
Models power draw under sustained real-time load, not burst benchmarks.
Architecture Explorer
Trades latency against power for your duty cycle.
Pre-Silicon Validation
Proves it on hardware before the robot does.
Manufacturing
Packaged, tested parts as our line comes online.
CLEAN FUTURE CF-1
CF-1 targets 20–32 TOPS at 3–5 W, with transformer support and multi-camera inputs — a co-processor that plugs in beside your robot's main compute board.
HOW TO START
NOT SURE YET
Find out whether custom silicon is worth pursuing before you commit budget.
Start with an assessment →
READY TO DEFINE
See the full system modelled and stress-tested against your workload.
Model your SoC →
READY TO BUILD
Start from Clean Future CF-1 or design an accelerator around your workload.
Build an accelerator →
Tell us what your robotics product needs to run, and the budget it has to run on.
Talk to us— From AI to Silicon
© [Year] PathinAI Technologies Private Limited