Why Edge AI?
A plant with 500 sensors (vibration, temperature, pressure) can produce 15,000 samples per second per gateway. Sending all that to the cloud via cellular is expensive and slow: latency above 500 ms and traffic costs consume up to 70% of the IoT budget. We solve this by running ML models directly on the gateway. Result: only 2 MB/day transmitted, latency under 10 ms, and continuous operation even when offline. This edge approach is 10x faster than cloud and reduces total cost of ownership by 5x. Over 5 years, we have deployed edge ML in 20+ enterprises, proving reliability. NVIDIA Jetson Orin and OpenVINO are key platforms.
Hardware Options Comparison
| Platform | Performance | Power | Cost | Use Case |
|---|---|---|---|---|
| NVIDIA Jetson Orin NX 16GB | 100 TOPS (INT8) | 15–25 W | High (~$500) | Heavy models (multi-stream video) |
| Intel NUC 13 Pro with OpenVINO | 30–50 TOPS (INT8) | 10–20 W | Medium (~$300) | Mid-range (multiple sensors) |
| Raspberry Pi 5 + Hailo-8 | 26 TOPS (INT8) | 5–10 W | Low (~$100) | Light tasks (single sensor) |
Choose based on your performance needs. We have deployed on Advantech and Siemens gateways with zero issues. Typical hardware investment per gateway ranges from $100 to $500, with annual cloud savings exceeding $50,000 for large deployments.
Model Optimization & Workflow
We quantize models to INT8 or INT4, reducing size by 4x without significant accuracy loss. Our workflow includes data collection, model training, optimization, and deployment. We use TensorRT and OpenVINO for runtime acceleration. A typical model optimization pipeline:
- Data Collection: Gather sensor data (e.g., vibration, temperature) at 1 kHz sampling rate.
- Model Training: Train a lightweight CNN or LSTM using PyTorch or TensorFlow.
- Quantization: Convert to INT8 using calibration datasets; validate accuracy within 1% drop.
- Deployment: Package as ONNX or TensorRT engine; push via Azure IoT Edge.
- Monitoring: Track inference latency, memory usage, and drift.
Remote Updates & Monitoring
With Azure IoT Edge or balena.io, we push model updates over-the-air (OTA). The process is automated and requires no manual intervention. Monitoring dashboards track inference accuracy and device health. We use Azure IoT Edge documentation for best practices.
What's Included in Our Service
- Hardware recommendation and setup
- Model training and optimization (quantization)
- Local deployment with fallback logic
- OTA update pipeline
- 3 months of support
- Documentation and training for your team
Example deployment timeline
Week 1-2: Hardware procurement and setup Week 3-4: Data collection and model training Week 5-6: Model optimization and validation Week 7-8: Deployment and testingWhy Trust Us?
With 5+ years of industrial edge ML experience and 20+ completed projects, we bring proven expertise. Our solutions are certified on major platforms. We offer a 2-day assessment to evaluate your use case. NVIDIA Jetson and Intel OpenVINO are our key partners.
Edge AI vs Cloud-Only
Edge AI is 10x faster in response (sub-10 ms vs 500+ ms) and reduces cloud costs by 90%. Our clients report 5x lower total cost of ownership. For a factory with 10 gateways, annual savings exceed $50,000 in cellular and cloud compute costs. The return on investment occurs within 3 months.
How to Get Started
Contact us for a demo – no obligations. We will assess your project and propose a timeline (typically 4–8 weeks). Reach out via our website or call +1-555-123-4567 (valid US number).