EDGE AI & MACHINE LEARNING / COMPUTER VISION

Akhila Labs deploys optimized inference models directly on your devices. Leverage TinyML, NPUs, and quantization to deliver privacy-first, real-time AI solutions. Lower latency, reduce bandwidth, control costs—all while respecting user privacy.

Case Study 3: Facility Occupancy Detection – Edge ML at Scale

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Challenge A smart building operator required occupancy detection across 200+ rooms to optimize HVAC systems. The solution needed to ensure complete privacy (no camera-based face data storage) while delivering accurate, real-time occupancy insights. Solution Sensor Fusion: Combined thermal sensors with motion detectors to eliminate the need for cameras. Lightweight AI Model: Small LSTM model (8KB, […]

EDGE AI & MACHINE LEARNING / COMPUTER VISION

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Case Study 2: Wearable ECG Anomaly Detection – Privacy-First Healthcare

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Challenge A remote patient monitoring wearable required real-time arrhythmia detection directly on the device, without uploading sensitive ECG data to the cloud. The system needed to ensure high accuracy, ultra-low power consumption, and complete data privacy. Solution On-Device AI Model: TFLite Micro model trained on PhysioNet ECG dataset and synthetic variants. Lightweight Optimization: Model compressed

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Case Study 1: High-Speed Defect Detection in Automotive Manufacturing

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Challenge A Tier-1 automotive supplier was producing machined metal parts at a rate of 2 parts per second. Their cloud-based vision system introduced 500ms+ latency, forcing production slowdowns. Additionally, missed micro-defects (less than 0.5mm scratches) were leading to costly warranty claims. Solution Edge Vision Hardware: Industrial global shutter camera integrated with an NVIDIA Jetson-based edge

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