DoiT and Pronti AI started working together at the beginning of the migration and redesign phase, enabling DoiT to identify the weaknesses and pain points of the previous architecture. Through its review, DoiT identified the critical areas of improvement: inflexible resource provisioning, complex maintenance, and weak security measures. DoiT proposed a tailored and simplified Google Cloud-based architecture that maximized the utilization of managed services and serverless components, shifting some of the expertise demand from Pronti AI to DoiT.
Due to the stringent resource provisioning process, DoiT first focused on improving scalability and elasticity. By adding Google Cloud Run, a serverless environment for containerized applications, Pronti AI could better handle demand fluctuations without managing the underlying infrastructure. The integration of Cloud Run with Google Cloud Build facilitated easy deployment and scaling of API services, ensuring scalability and elasticity to meet varying workloads. To ensure efficient database schema upgrades, DoiT supported Pronti AI in setting up Cloud Run Jobs for Database Schema upgrades. These isolated and ephemeral environments allowed independent execution of schema upgrade tasks, minimizing the impact on the main application’s performance and availability. The scalability, resource efficiency, visibility, error handling, rollbacks, integration with CI/CD pipelines, and security features of Cloud Run Jobs streamlined the schema upgrade process.
To automate the build, testing, continuous integration and deployment of API services, DoiT’s Customer Reliability Engineers made a comprehensive and strategic recommendation to integrate Google Cloud Build with GitHub repositories. This integration helped Pronti AI to ensure that its API services were always up to date, eliminating the need for manual deployments. The integration enabled CI/CD, enhancing the development process’s efficiency and reducing the effort required. Cloud Build also enabled Pronti AI to define custom build steps and configurations for its API services. This flexibility empowered them to tailor the build and deployment workflows to its requirements. They could now set up build triggers, define environment variables, run tests, and execute custom scripts, creating extensive customization opportunities during development. And by deploying API services via Cloud Build integration with GitHub repositories, Pronti AI could now leverage Cloud Run’s built-in monitoring and logging capabilities. This integration provided valuable insights into its API services’ performance, availability, and error rates. They could utilize Google Cloud Logging to capture and analyze logs for efficient debugging and troubleshooting. By leveraging GitHub’s popular version control platform and integrating it with Cloud Build, Pronti AI gained the benefits of version control for its API services, such as branching, pull requests, and code reviews. This integration streamlined collaboration, enabled effective change management, and ensured a well-documented codebase history. The strategic guidance that Pronti AI received from DoiT engineers enabled this small IT staff to improve development with customizations and automations that helped drive both quality of deliver and efficiency.
To finalize the migration and optimization, DoiT provided expertise to develop the security around the infrastructure. To enhance security and network isolation, DoiT guided the configuration of Pronti AI’s database to use an internal IP and connected it through a VPC Serverless Connector. This approach offered enhanced security, reduced attack surface, improved performance and latency, compliance with regulatory requirements, simplified networking, and seamless integration with other VPC resources.
