Enabling Edge AI Across the Product System
AI features are only useful when the surrounding product system makes them practical. One initiative I worked on at Control4 connected existing hardware, an edge AI accelerator, model-training data, cloud video processing, and a customer-facing mobile experience.
My role focused on systems engineering and cross-team leadership, not building the machine-learning models themselves.
Adapting an existing controller
I partnered with hardware engineers to explore how an existing CORE1 controller could be modified to run a Hailo edge AI accelerator. Reusing an established controller platform meant working within real hardware and software constraints rather than designing an entirely new device around inference.
Enabling collection of training data
I developed a driver that employees and beta dealers could install to contribute images from their cameras for model training.
The training effort focused on recognizing people, cars, and animals across times of day and environmental conditions. The driver was an enabling piece of the system: it connected real-world cameras to the data needed to develop and evaluate the models.
Bringing detections into the mobile experience
I had the mobile teams build a History section to help customers see what had been happening around their homes. We integrated edge AI detections into that workflow so that a detection could become meaningful information in the product experience, not just an event generated by a device.
Making video processing more economical
As the cost of converting video clips increased, I worked with the Cloud team to replace the Amazon-based video conversion approach with a custom FFmpeg processing pipeline.
That change dramatically reduced conversion costs. I don’t have a verified percentage to publish, but the experience reinforced the importance of evaluating ongoing operating costs as part of system design.
What I take from this work
This was a systems problem that crossed several disciplines:
- Hardware and embedded software: enabling inference on an existing controller platform
- Model development support: collecting representative images from real deployments
- Mobile product development: bringing detections into an understandable History workflow
- Cloud infrastructure: finding a more economical way to process video clips
The important leadership work was recognizing these dependencies, helping teams connect their pieces, and making sure the technology could fit into a useful and sustainable product experience.