THE CHALLENGEWhy Edge AI Matters for Real Products
Cloud AI works well for many applications. But when your product needs sub-10ms inference latency, works in environments with intermittent connectivity, processes sensitive data that should stay local, or needs to run on battery power for years, you need AI at the edge. The gap between a trained model and a production edge deployment is wider than most teams expect. We bridge that gap.
DEPLOYMENT TARGETS
MCU, SBC, or GPU: We Deploy on All Three
Microcontrollers (TinyML)
STM32, ESP32, nRF
AI inference on devices with kilobytes of RAM. We deploy quantized models using TensorFlow Lite Micro and STM32Cube.AI for tasks like keyword spotting, gesture recognition, anomaly detection, and simple classification. When your device runs on batteries and every milliwatt counts, TinyML is the answer.
RAM: 64KB to 1MB, Flash: 256KB to 2MB, Inference: 10ms to 500ms
Linux SBCs
Raspberry Pi, BeagleBone, Custom
When your model needs more compute than an MCU can provide, but cloud latency or connectivity makes server inference impractical. We deploy models on Linux SBCs using TensorFlow Lite, ONNX Runtime, or custom C++ inference engines. Common for camera-based inspection and audio processing.
RAM: 512MB to 8GB, Storage: 8GB+, Inference: 5ms to 200ms
Edge GPU
NVIDIA Jetson, Hailo, Coral
For computer vision workloads that need real-time performance on multiple camera streams. We optimize models with TensorRT, deploy on Jetson Orin/Xavier, and build complete inference pipelines with pre/post-processing. Multi-stream video analytics, defect detection, and safety monitoring live here.
TOPS: 4 to 100+, Inference: <5ms, Multi-stream: 4 to 16 cameras
WHAT WE DELIVER
From Trained Model to Production Device
Model Optimization
We take your trained model and make it run on target hardware. Quantization (INT8, FP16), pruning, knowledge distillation, and architecture search to hit your latency and accuracy targets. We measure the real tradeoffs so you can make informed decisions.
Runtime Integration
We integrate inference engines into your product firmware or application. TensorFlow Lite, TensorFlow Lite Micro, ONNX Runtime, TensorRT, and STM32Cube.AI. Proper memory management, threading, input preprocessing, and output postprocessing.
Continuous Learning Pipelines
We build the infrastructure for collecting field data, retraining models, and deploying updated models to devices via OTA. Version management, A/B model testing, and performance monitoring so your edge AI gets better over time.
Performance Benchmarking
Before committing to hardware, we benchmark your model across target platforms. Latency, throughput, accuracy, power consumption, and thermal behavior. You get a clear picture of what is achievable before making production hardware decisions.
Sensor-to-Inference Pipeline
We build the complete data path from sensor input to model output. Camera capture and ISP configuration, microphone array processing, accelerometer data windowing, and all the preprocessing that turns raw sensor data into model-ready tensors.
Production Hardening
We make sure edge AI runs reliably in production. Watchdog timers for inference timeouts, graceful degradation when models fail, telemetry for monitoring inference quality, and automated recovery from edge cases that trip up the model.
USE CASES
Where We Deploy Edge AI
Visual Inspection
Defect detection on production lines where milliseconds matter and cloud round-trips are too slow. We deploy object detection and classification models that run at line speed on Jetson or custom vision hardware.
Predictive Maintenance
Vibration analysis, current signature monitoring, and acoustic anomaly detection running directly on the equipment. Models detect bearing wear, motor faults, and pump cavitation before failures happen.
Anomaly Detection
Autoencoder and isolation forest models deployed on MCUs and SBCs for real-time anomaly detection. We train on normal operation data and deploy models that flag deviations in sensor readings, power consumption, or process parameters.
Voice and Audio Processing
Keyword spotting, speaker identification, and audio event detection on microcontrollers. We deploy models that run continuously on battery-powered devices, waking the system only when relevant audio events are detected.
Ready to Put AI on Your Device?
Tell us about your model and target hardware. We will assess feasibility, benchmark performance, and give you a clear path from prototype to production edge deployment.
Schedule a Free Consultation