Eigen Control develops AI-assisted Raman analysis, industrial chemistry inference, and GPU-native simulation tools for faster quality, control, and engineering decisions.
The refinery's eigen-frequencies are where control starts: Fourier math finds the modes that ring in the process and turns them into levers a controller can move.
The platform connects process data, optical measurements, numerical methods, and modern compute—turning complex engineering signals into decisions operators and technical teams can use.
Real-time inferential quality from existing process signals. Build reliable chemistry estimates for monitoring, advisory control, and closed-loop applications—without waiting on delayed laboratory results.
Industrial deployment pathApplied mathematics and hybridizable discontinuous Galerkin methods designed for accurate, scalable scientific computing.
New math for new hardwareScientific workloads architected to exploit the parallel compute infrastructure already reshaping AI development.
Cluster-native architectureEigen is designed around the system boundary that matters: instruments and process tags in, trusted quality values and engineering decisions out.
Raman spectra, DCS tags, laboratory reference data, and operating context.
Data conditioning, signal health checks, and secure local model execution.
Physics-informed models estimate live composition, quality, or field variables.
Quality values, KPIs, advisory recommendations, and control-ready outputs.
Every model is judged by the decisions it improves: quality, yield, throughput, energy, uptime, engineering cycle time, or safe operation.
Live quality inference, Raman-based composition, and process-control support for complex hydrocarbon systems.
Quality · yield · controlFaster simulation for carbon capture, renewable fuels, emissions, water systems, and next-generation energy assets.
Design · scale-up · riskHigh-performance fluid analysis for aerodynamics, thermal systems, and engineering certification workflows.
Speed · fidelity · iterationSimulation and analytical intelligence for semiconductors, reactors, materials processing, and complex production systems.
Throughput · consistency · scaleLegacy CFD software was shaped by the limitations of earlier hardware. Eigen rewrites the solver stack around AI linear-algebra platforms, parallel numerical methods, and multi-node GPU systems.
TensorFlow and PyTorch provide access to highly optimized GPU operations and scalable execution.
Numerical schemes selected for accuracy, parallelism, and efficient solution of demanding flow problems.
An architecture intended to scale scientific computing across the same cluster class used for AI training.
Advanced technology earns trust through rigor, integration, and clarity—not visual spectacle or opaque claims.
Models are developed with the physical and chemical structure of the problem in view—so predictions remain meaningful to engineers.
Software and numerical methods are designed together for edge inference, GPU execution, and modern parallel infrastructure.
The end product is not a model file. It is a dependable signal, solver, or decision workflow that fits the plant and engineering stack.
We provide physics and chemistry computational platforms for planning and control of real-time industrial systems—where better visibility and faster engineering create measurable operating value.
