Edge AI Solutions
Bring AI to the edge for real-time inference, reduced latency, and privacy-preserving processing where your data lives.
AI Where It Matters Most
Not all AI belongs in the cloud. Edge AI enables real-time decision making, reduces bandwidth costs, and keeps sensitive data local. We help you optimize and deploy models on resource-constrained devices.
- Model optimization and quantization for edge deployment
- Custom hardware acceleration (GPU, TPU, NPU)
- Embedded systems and IoT integration
- Hybrid edge-cloud architectures
Our Edge AI Capabilities
Model Optimization
Quantization, pruning, and knowledge distillation to shrink models without sacrificing accuracy.
Hardware Acceleration
Optimize inference for specific hardware: NVIDIA Jetson, AWS Inferentia, AWS Trainium, and more.
Embedded Deployment
Deploy models on microcontrollers like ESP32 and STM32 with TinyML frameworks.
Edge-Cloud Sync
Hybrid architectures that combine edge inference with cloud training and model updates.
Real-Time Processing
Sub-millisecond inference for time-critical applications like autonomous systems and robotics.
Privacy-First AI
Keep sensitive data on-device with federated learning and local inference.
Offline Capability
Run AI without connectivity for remote, disconnected, or air-gapped environments.
Power Efficiency
Optimized inference for battery-powered and energy-constrained edge devices.
Ready to Deploy AI at the Edge?
Let's discuss how edge AI can reduce latency and bring intelligence closer to your data.
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