We specialize in large-scale computational fluid simulations and stochastic optimization for the energy sector. From sub-surface modeling to CO2 sequestration, we build the infrastructure that powers complex science.
Modeling irregular grid geometries and sub-surface fluid behavior for CO2 sequestration and oil and gas extraction.
Developing high-concurrency optimization frameworks for real-world datasets and high-variability physics.
Specialized expertise in migrating heavyweight legacy runtimes to Kubernetes, focusing on build-tiering, OCI optimization, and delivery velocity.
We successfully manage a larged-scale (270KLOC) scientific monorepo using specialized high-performance runtimes. Our architecture leverages tiered build strategies to achieve a reduction in latency and a massive speedup in cluster synchronization.
We combine the expressive power of high-level dynamic languages with the strict operational requirements of modern DevOps.
We treat our scientific workloads as first-class citizens in the Kubernetes ecosystem, ensuring high availability and automated scalability.