Project Background

In the investment finance industry, risk management and regulatory compliance are a crucial aspect of operations. In this project, we built a multi-tenant SaaS platform that utilises artificial intelligence to automate and analyze risk.

The platform was deployed on DigitalOcean, using Kubernetes and Terraform to manage the infrastructure.

The platform's architecture is based on a microservices approach, where each service is responsible for a specific functionality. This allows for greater scalability and flexibility in managing the resources required for each tenant.

To handle real-time data access, we used the Go programming language, known for its high performance and efficient memory management. Asynchronous programming was used to handle high-performance request processing.

For multi-tenancy, we used a multi-tenant architecture that allows for efficient management of resources, including memory, CPU and storage. This also helps in cutting down deployment and server costs.

For the AI pipeline, we used a combination of machine learning and deep learning techniques to analyze data and identify potential investment risks. This pipeline was integrated with the platform, allowing tenants to access the AI functionality as part of their subscription.

To deploy the platform, we used Terraform to provision the infrastructure on DigitalOcean. Kubernetes was used to manage the containerised microservices, allowing for easy scaling and deployment of updates.

To tackle the complex regulations and data access requirements in the investment finance industry, we conducted extensive research to understand the specific requirements and tailor the technology accordingly.

In summary, the Multi-Tenancy SaaS platform uses AI to automate and analyze risk, is deployed on DigitalOcean, and managed via Kubernetes and Terraform. The platform's architecture is based on microservices, which allows for scalability and flexibility.

The platform is built with Golang to leverage asynchronous programming, and multi-tenant architecture to handle real-time data access and high-performance request processing. A full AI production pipeline was also deployed as the core of the platform.

The platform was successfully deployed in a highly regulated environment with complex data access and migration requirements.