Most businesses face a common challenge when the longer their platform exists, the harder it becomes to scale them over time. Especially it gets harder to integrate new features when data volumes and user numbers increase, since they put pressure on the architecture that wasn’t build for the load.
This is the exact problem our client with a real-time video processing platform faced with. Their attempts to address the growing demands in-house failed because of technical limitations, so for the further upgrades, the platform needed a partial redesign of the system and additional backend and DevOps expertise.
When we joined the project, the platform was already in operation, but the system still required scaling and optimization of certain modules. Growing video volumes were exposing the platform’s limits:
- High-load real-time video processing;
- Outdated libraries;
- Compatibility with different IP cameras;
- Keeping multiple simultaneous video streams stable.
Our priorities for the project were based on the client’s business goal, to build stable, yet scalable AI platform for real-time video analysis and object tracking:
- Stable video data processing;
- High system performance;
- Scalable architecture;
- Support for modern AI tools;
- Rapid integration of new features.
The project team consisted of 5 developers, 5 QA engineers, and 1 product manager. We worked closely with the client throughout the development process, using regular online meetings and task trackers to keep them up to date on progress and new features.
Development was carried out iteratively using an Agile approach. Tasks were organized into sprints, and code reviews, testing, and infrastructure updates were conducted on a regular basis. Some of the tasks involved the gradual refactoring of the system’s legacy components.
Key Solutions We Delivered
1. More Reliable Face Recognition
We extended IP-camera support to make the system detect and capture faces more reliably not only in low-light but also in a crowded environments. This also allowed the system automatically process video streams, detect faces, and save the data for further analysis.
We built the backend with Java 17 and Spring Boot, ensuring a stable foundation for video services. As a result, the system could recognize people and objects more accurate in crowded environments and the capabilities of video analytics were expanded.
2. Faster Search Across Video Data
Our team implemented a flexible search function for images and object parameters within the platform using the Elasticsearch. It allowed users to search across images, object parameters, trajectories, and events, making video analytics faster to navigate.
3. Centralized Logging and Monitoring
Using RabbitMQ and Kibana we implemented centralized logging. Enabling message flow between services, and providing a clear, centralized view of system logs improved system visibility, so it become much faster to detect issues and find their root causes.
4. Gradual System Modernization
Instead of a full rewrite, we modernized the system step by step, replacing outdated components without interrupting ongoing development.
We used Docker to standardize environments and simplify deployments, while Jenkins automated CI/CD pipelines. The codebase was managed in GitLab, ensuring a smoother and more reliable delivery process.
Results and Impact
- More stable operation under high video-stream loads;
- Faster processing and searching of platform data;
- Reduced time spent analyzing logs and investigating errors;
- Improved performance of key backend modules;
- Better support for new IP cameras and video-analysis scenarios;
- A more maintainable foundation for future AI features.
While working on the project, we didn’t only update legacy components and added new functionality, but we’ve also strengthened the infrastructure step by step, without disrupting what was already running.
Our background in Java and Spring Boot, high-load backend systems, DevOps, and CI/CD was what the client needed to move from an outdated architecture to a more stable and flexible base that can be modernized later on.
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