Edge AI Platform Engineering
Operate AI on hardware in the field. From Jetson and DGX Spark to Raspberry Pi fleets: lightweight Kubernetes, OTA model updates, remote observability, and secure provisioning.
How it works
From build to field operations: one consistent edge fleet lifecycle.
What we build
- K3s / MicroK8s at the edge with fleet provisioning
- OTA model & container updates with rollback
- Optimized model delivery (TensorRT, ONNX)
- Remote observability and troubleshooting
- Secure bootstrapping and hybrid cloud ↔ edge sync
Scope
We handle
- Edge Kubernetes & fleet management
- OTA update pipelines
- Edge observability & remote ops
- Cloud ↔ edge sync and policy
Better with a partner
- Custom firmware / embedded
- Sensor & hardware integration
Typical engagement
From first device to fleet operations: one consistent lifecycle for edge inference at scale.
- 01
Fleet assessment
Review hardware mix, connectivity, update constraints, and cloud ↔ edge requirements.
- 02
Platform bootstrap
Provision K3s or MicroK8s fleets, package models (TensorRT, ONNX), and onboard devices securely.
- 03
OTA & observability
Build update pipelines with rollback, remote monitoring, and field troubleshooting playbooks.
- 04
Fleet operations
Optional retainer for rollout support, fleet health reviews, and platform upgrades.
Ready to take your AI workloads to production?
Let's talk about your platform: cloud, hybrid, or edge. Start with a short, no-pressure conversation.