Leo
Summary
As part of an internal R&D initiative, I led the design initiatives of a video surveillance platform that integrates standard IP cameras with an AI-enabled edge computing device. The system enhances traditional surveillance by running real-time analytics directly on the edge box, enabling features such as object detection, activity recognition, and anomaly alerts without relying on constant cloud connectivity. This hybrid approach improves response times, reduces bandwidth costs, and supports data privacy by processing sensitive footage locally. The project was a proof-of-concept aimed at modernizing legacy surveillance infrastructure through scalable, intelligent edge solutions.
Challenge
One of the core challenges of this project was to design a video surveillance solution that stood out in a highly competitive market. We needed to clearly define what would make our platform uniquely valuable to customers.
To address this, we began with a deep dive into market research, analyzing existing solutions, identifying pain points in current offerings, and mapping out industry trends. This helped us identify clear areas where we could innovate.
Outcome
What set us apart was our dual capability: expertise in both hardware and software. Unlike many competitors who relied solely on cloud processing or off-the-shelf AI models, we had the in-house experience to design a custom edge box optimized for AI workloads and tightly integrated with our software stack. This allowed us to deliver high-performance, real-time insights directly at the edge, reducing latency, improving reliability, and protecting sensitive data.
By leveraging this unique combination of hardware and software expertise, we carved out a competitive advantage—offering a platform that not only matched industry standards in AI-driven surveillance but exceeded them in performance, flexibility, and privacy control.
