🏭 India's Industry 4.0: The Smart SME

India's massive SME manufacturing hubs—like the textile looms in Tirupur or the auto-parts factories in Pune—are largely analog. Machine downtimes are logged manually, and quality assurance relies solely on human vision grading.

The transition to a "Smart Factory" does not require replacing 20-year-old heavy machinery with new hardware. It requires Edge Retrofitting.

Edge AI and Sensor Arrays

By attaching an inexpensive vibration sensor to an industrial motor array and wiring it to a Raspberry Pi, we can instantly digitize legacy equipment.

1. Predictive Maintenance: The baseline vibration data is fed into an Edge AI model directly on the factory floor (to avoid cloud latency and bandwidth costs). The model learns the 'normal' frequency. When a bearing begins to fail, the frequency shifts fractionally; the model flags this immediately and alerts the maintenance crew via a React Native app before the motor shreds itself.
2. Computer Vision QA: Traditional assembly lines use manual spot checks. We deploy low-cost webcams over conveyor belts running TensorFlow.js locally. The webcams scan every single widget passing underneath at 60 frames per second, instantly calculating microscopic geometric defects and triggering a pneumatic rejection arm.

Bringing this level of sophisticated, local compute to the Indian SME sector drastically slashes overhead costs and skyrockets global export quality.