AI with hardware

Edge AI, vision, and audio automation

Camera, audio, and sensor intelligence on Jetson Nano-class devices, embedded Linux systems, and cloud-assisted AI workflows.

Use this when automation needs perception: object detection, plant condition detection, visual inspection, scoreboard reading, audio event recognition, or AI-assisted operator support.

Typical scope

  • Jetson Nano, GStreamer, OpenCV, custom ML models, AWS APIs, OpenAI and Claude API integration
  • Camera ingest, inference loops, alerting, dashboards, data collection, and model iteration plans
  • Practical split between edge inference and cloud AI so latency, cost, and reliability stay realistic

Questions this page answers

Can you train custom models?

Yes, when there is enough image, video, or audio data. When data is thin, the first scope is usually data capture and labeling.

Can this run without internet?

Often yes for local detection. Cloud APIs are used only where they add value.

Next step

Send a small brief.

Include the board, sensors, current failure, desired behavior, and the artifact you need: outsourced automation help, working firmware, a control box, an architecture review, a human-in-the-loop workflow, or a vendor handoff packet.