Overview
Babcom’s Edge AI services bring intelligence closer to the data source—empowering devices to make real-time decisions without relying on cloud connectivity. We design and deploy scalable, low-latency, and resilient edge AI systems tailored for industrial, smart infrastructure, automotive, and healthcare use cases.
What We Offer
- AI Model Deployment at the Edge
Compact LLMs, vision transformers, and time-series models optimized for edge inference on NVIDIA Jetson, ARM, Intel Movidius, and RISC-V. - Edge Containerization
Modular, secure deployments using Docker, Podman, and K3s on constrained edge hardware for faster rollouts and scalable orchestration. - Real-Time Decision Engines
Event-driven architectures processing sensor data with sub-100ms response times using MQTT, Kafka, and Pulsar. - Edge Protocol Gateways
Translate BLE, Modbus, OPC UA, CAN, BACnet, and proprietary M2M protocols into a unified data fabric for seamless integration. - Edge-Based Asset Intelligence
Real-time asset localization and tracking using UWB, RF triangulation, GPS, and sensor fusion. - AI Optimization & Compression
Model pruning, quantization, and distillation for efficient edge inference with low power and memory consumption.
Industries We Serve
- Smart Manufacturing: Predictive maintenance, defect detection
- Infrastructure & Utilities: Decentralized control, load balancing
- Connected Mobility: Real-time diagnostics, fleet edge AI
- Healthcare: Local health monitoring, anomaly detection
Why Babcom?
- Deep expertise in embedded systems & Edge AI
- Proven deployments across cloud-edge ecosystems (Azure IoT Edge, AWS Greengrass, GCP)
- Fast prototyping, resilient architectures, and secure OTA model updates
