How does
Atreides
Use AI?
Achieved significant cost reduction and operational control for defense workloads.
Project Overview
Processing large volumes of intelligence data to identify patterns and threats for military customers, enabled by a scalable, cost-effective, and secure infrastructure.
Layman's Explanation
Imagine you are a security guard for a massive digital city, watching countless video feeds at once. Atreides built a system to automatically flag strange events, but their control room was a mess. They hired specialists to build a new, super-organized control room that can instantly add more screens when things get busy and shrink back down when it is quiet, all while saving on the electricity bill and keeping the room's doors locked tight.
Details
Atreides needed to modernize its infrastructure for processing sensitive intelligence data for 5-EYES military customers. Their existing ad-hoc system lacked operational control, was expensive to run, and could not scale reliably to meet the demands of defense-grade big data workloads.
Revela implemented a cloud-native solution centered on a self-managed Apache Spark deployment on Kubernetes, which replaced the more restrictive and costly AWS EMR. This provided a flexible, cloud-agnostic data processing engine. The entire infrastructure was defined using Pulumi, an Infrastructure-as-Code tool, enabling automated and repeatable deployments through a GitOps workflow with GitHub Actions. This gave Atreides the operational control they lacked.
A key innovation was the use of Karpenter, a Kubernetes autoscaler, to dramatically reduce costs. Karpenter intelligently provisioned compute resources just-in-time, making extensive use of cheaper AWS Spot Instances for bursty, fault-tolerant data processing jobs and scaling down to zero when idle. For security, the architecture was designed for air-gapped operation, using Tailscale for zero-trust networking and a comprehensive monitoring stack including Loki, Grafana, and Tetragon to ensure observability and compliance within the high-security defense environment.
Analogy
This is like upgrading a popular food truck's kitchen. They were cooking complex meals, which is processing data, but in a cramped, inefficient space. The upgrade gave them a modular, state-of-the-art kitchen, which is Kubernetes with Spark, that expands or shrinks based on the number of orders, which is Karpenter auto-scaling. It also uses cheaper energy sources when available, which are spot instances, and has top-notch security, all documented in a clear recipe book, which is Infrastructure-as-Code.
Machine Learning Techniques Used
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Technology
Novelty Justification
The project represents a sophisticated and complex infrastructure engineering feat, combining a modern, self-managed Spark on Kubernetes architecture with advanced cost optimization (Karpenter) and automation (IaC) within a highly secure, air-gapped 5-EYES defense environment.
Project Estimates
