Not every model can live in the cloud. We prototype and deploy inference systems on embedded hardware for field stations, hatcheries, farms, and anywhere connectivity is limited.
What We Do
- Edge inference deployment on embedded hardware
- Computer vision inference pipelines for RGB, infrared, and ultrasound imaging
- Model inference with structured output data upload to AWS/GCP
- Locally hosted LLM deployment with API and web interface for private, on-premise AI
- Sensor integration and real-time data processing
- Hardware selection, prototyping, and field-ready enclosure design
- Low-power ML model optimization (quantization, pruning, INT8/FP16)
- Over-the-air model updates and remote fleet management
- IoT data pipelines from edge to cloud
Hardware Platforms
We work across the full spectrum of edge devices, matched to the performance, power, and cost requirements of each deployment.
Microcontrollers and Low-Power Devices
- ESP32: Sensor hubs, BLE/WiFi data loggers, environmental monitoring, ultra-low-power deployments
- Raspberry Pi Zero / Zero 2 W: Compact camera traps, lightweight inference, remote monitoring nodes
Single-Board Computers
- Raspberry Pi 3 / 4 / 5: General-purpose edge inference, image classification, data collection stations, lab automation
- Raspberry Pi with Hailo-8L HAT: Hardware-accelerated inference (up to 13 TOPS) on standard Pi form factor for real-time object detection, species ID, and phenotyping
GPU-Accelerated Edge
- NVIDIA Jetson Nano: Entry-level GPU inference for field-deployed computer vision
- NVIDIA Jetson Orin Nano: Mid-range edge AI (up to 40 TOPS) for multi-camera systems, real-time video analysis, and autonomous monitoring
- NVIDIA Jetson AGX Orin: High-performance edge inference (up to 275 TOPS) for multi-model pipelines, robotics integration, and demanding field workloads
Datacenter and Training
- NVIDIA DGX systems: Multi-GPU training infrastructure for model development before edge deployment; we design the full pipeline from DGX training through edge-optimized inference
Deliverables
- Working prototype with documented hardware BOM and wiring diagrams
- Optimized inference model packaged for target hardware (TensorRT, ONNX, TFLite)
- Field deployment guide with power, networking, and environmental specifications
- Monitoring dashboard for device health and inference metrics
- Source code and deployment scripts for reproducibility
Need inference at the point of data collection? Let’s talk.
