Our client needed an intelligence data analysis tool built in Elixir with the Phoenix Framework to deliver real-time visualization and machine learning–driven query results. The platform had to handle large volumes of intelligence data, extract the most relevant insights, and present them to users instantly. Beyond application logic, the project required a secure, scalable infrastructure and strong development practices to support multiple environments and continuous delivery.

We designed and implemented a highly concurrent data pipeline using Elixir and the Oban library, enabling advanced query capabilities like named-entity recognition and location-based searches. To improve efficiency, we applied many bespoke enhancements to the Elixir language and libraries used by our team, which resulted in cleaner code, reduced memory usage and streamlined execution. This ensured the system could process data reliably and at scale while remaining flexible for future enhancements.

On the infrastructure side, we built a fully automated AWS environment using Terraform, replacing CloudFormation to achieve greater flexibility and repeatability. The architecture leverages EC2, S3, RDS, and OpenSearch, deployed in secure private subnets with load balancers and NAT gateways. Integrated with CircleCI, the setup supports hands-free deployments, while Credo and Prettier enforce code quality across Elixir and JavaScript, ensuring consistent and reliable releases.

Although not yet in production, this solution has established a scalable, secure, and automation-driven foundation for the client’s platform. With its concurrent pipeline and resilient infrastructure, the system is now well-positioned for performance optimization, monitoring, and eventual production rollout as a robust intelligence analysis tool.