




Mission: To design and deliver affordable, intelligent sensing systems that enable industries to monitor motor health in real time, detect faults early, and achieve predictive maintenance without disruption.
Vision: To become a global leader in AI-powered industrial diagnostics, making advanced predictive maintenance accessible to every factory floor — from SMEs to large-scale enterprises — and driving a future of zero unplanned downtime.

Real-time acquisition using low cost sensor and microcontroller

Transform time-series, multi-dimensional signals into high-resolution, multi-dimensional 3D images to simplify feature extraction and fault identification

ML models trained on labeled fault conditions to learn healthy vs faulty states

Efficient streaming to cloud dashboard for storage

Compact sensor installation with easy plug-and-play architecture

Low-cost, modular hardware with scalable deployment
01
Continuously monitors motor vibrations using high-precision low cost sensors, enabling early
identification of abnormal operating conditions before failures occur.

02
Processes advanced signal processing techniques to allow for more accurate and reliable fault
detection compared to conventional threshold-based methods.

03
Uses trained machine learning models to detect and classify motor faults, providing clear health
status and fault type identification instead of sensor readings.

04
Streams data to the cloud for real-time analysis and long-term storage, enabling trend analysis,
historical comparison, and smarter maintenance planning.

05
Compact, modular sensor design allows easy installation on existing motors, resulting in quick
deployment without production downtime or system modifications.

06
Simple visual dashboards convert complex data into actionable insights, helping maintenance teams
make faster and better-informed decisions.

07
Low-cost hardware combined with cloud scalability ensures the solution is suitable for small
facilities as well as large industrial plants.

08
Early fault detection and health trend prediction support condition-based maintenance, leading to
reduced downtime, lower maintenance costs, and improved asset life.
