Edge AI & IoT — Real-time Intelligence on Devices
Deploy low-latency AI at the edge to act instantly on critical signals. We build optimized models, device integrations, and orchestration for fleet-scale edge intelligence.
Our Edge AI Capabilities
From on-device models to fleet orchestration — build reliable, low-latency AI that runs where decisions happen.
Real-Time Inference
Optimized model serving on-device for immediate decisions and closed-loop control.
Low-latency · Deterministic
Computer Vision
Defect detection, OCR, and visual monitoring with multi-camera and on-device processing.
CV · Line-side ready
IoT Integration
Sensor ingestion, telemetry pipelines, MQTT/OPC-UA connectors and device management.
Connectors · Robust
Model Optimization
Quantization, pruning, ONNX/TensorRT optimizations for size and speed on edge hardware.
TinyML · Efficient
Edge + Cloud Orchestration
Over-the-air updates, model rollout, telemetry aggregation and centralized monitoring.
Fleet ops · Scalable
Edge Security & Resilience
Secure boot, encrypted telemetry, authentication and fail-safe behaviors for edge deployments.
Secure · Resilient
Need edge AI that actually works on the floor?
We scope pilots, validate on-device performance and roll out fleet-wide.
Edge Platforms & Tools
Hardware
NVIDIA Jetson, Coral Edge TPU, Intel Movidius, ARM-based SoCs
Runtime & Optimization
TensorRT, ONNX Runtime, TFLite, OpenVINO, Edge SDKs
Edge Orchestration
Kubernetes (K3s), AWS Greengrass, Azure IoT Edge, Device management & OTA
Edge DevOps & MLOps
CI/CD for edge firmware and models, automated benchmarking, shadow testing and telemetry-driven rollouts to keep edge models healthy and performant.
Benefits of Edge AI
- Immediate, local decision-making with minimal latency
- Reduced bandwidth & cloud costs through local inference
- Improved resilience — offline capable and secure
- Scalable fleet management and centralized monitoring
Ready to Deploy Edge AI?
Talk to our engineers to design edge solutions that perform reliably at scale.
Contact Us