Find the fraction that carries the signal
Lightscline identifies the 5–10% of sensor data that matters and builds compact models around it—reducing infrastructure without sacrificing inference quality.
ON-SITE INDUSTRIAL ANALYTICS
On-site equipment intelligence—without cloud-scale compute.
Turn high-frequency vibration, acoustic, current, and thermal data into near-real-time equipment decisions using compact AI models built for the plant.
MaritimeManufacturingEnergy
Up to 435×less model compute
95%+accuracy on benchmark tasks
$4 MCUedge inference demonstrated
WHERE LIGHTSCLINE FITS
One workflow from raw sensing to operational intelligence.
Built for domain experts who need to discover, validate, and deploy meaningful signatures without adding a matching amount of infrastructure.
Move beyond binary anomaly flags
Support multi-class fault identification, remaining-useful-life prediction, and real-time fault progression across compressors, turbines, presses, motors, and rotating equipment.
Accelerate the stack you already have
Deploy through industrial analytics and software channel partners as a high-frequency accelerator—not a rip-and-replace plant platform.
FAULT SIGNATURE EXPLORER
Different faults leave different spectral fingerprints.
Lightscline learns compact representations that distinguish normal operation from ball, inner-race, and outer-race defects.
Selected signature: Normal operation
EDGE-COMPUTE PROOF
Less data makes training feasible on constrained hardware.
Smart sampling reduces training and transfer-learning time across Jetson Nano and Intel systems.
PROOF POINT
Peer-reviewed efficiency that reaches the edge
Published Scientific Reports benchmarks showed up to a 435× reduction in FLOPS versus a conventional CNN, with compact inference demonstrated on a Raspberry Pi Pico with 264 KB of RAM.
Discuss your applicationBUILT FOR YOUR SENSOR STACK