Case studies

Transform Infrastructure AI & Vision for Real-Time Insights - Codvo's Breakthrough

About the client

The client is a leading provider of AI-based security solutions that utilize computer vision to enable codeless development and deployment of machine learning models and AI applications.

Their platform offers near real-time detection of safety gear, goggles, and facial recognition, among others, for critical infrastructure management in manufacturing and energy plants.


The client had developed a computer vision model as part of their AI-based security platform solution that was intended to help detect safety gear, goggles, and facial recognition in near real-time. The current solution, which was connected to five cameras, faced challenges such as lag time in rendering and processing, and scalability to input from more than 100cameras.

Business Challenge

The client was facing two main challenges with their existing AI-based security platform solution. Firstly, their solution was unable to handle input from more than 5 cameras, resulting in lag time in rendering and processing. Secondly, the solution was not scalable enough to handle input from over 100 cameras, which limited the potential growth of the client's business. The client needed a solution that could address these challenges and enable them to scale up their platform while maintaining real-time processing capabilities.

Our Approach and Solution

Codvo Team undertook an in-depth analysis of the client’s AS-IS architecture and existing C# ML code to optimize and scale the platform. Our team optimized image processing by moving from bitmap to CV2 functions and storing images as objects rather than B64 strings. We also pushed frames to the platform rather than pulling to avoid RTSP-induced delay and manage data streams from multiple cameras using NATS. Additionally, we connected additional SSD drives for storage in an on-prem server and added GPU for on-prem model retraining and deployment.

Tech Stack

We utilized the following tech stack to optimize and scale the platform: C#, OpenCV, NATS, Kubernetes, Minio Object Storage, PostgreSQL, and Windows Server.


Business Impact

The solution provided by had a significant impact on the client's business.

By optimizing and scaling the platform, we enabled the client to process input from more than 20 cameras in real-time, removing any lag in image processing.
This increased the efficiency and reliability of the platform, resulting in faster and more accurate detection of safety gear, goggles, and facial recognition in critical infrastructure management.
Our solution allowed the client to scale up their operations, expanding their market share and increasing their revenue.
The on-prem architecture that we built was scalable and could be easily moved to a cloud platform, providing the client with greater flexibility and future-proofing their technology.

Overall, our approach helped the client achieve their objectives, resulting in a more efficient, reliable, and profitable business.