Industrial Data Science is never a simple set of AI model or software problems — it is always a complex set of multi-modal physics, process, and people problems.
Our highly differentiated AI|ML practice pairs three decades of Scientific Machine Learning research with operators who applied AI|ML techniques to real world problems, long before Sam Altman (OpenAI) had finished middle-school.
Three decades of esoteric consulting spanning Academia, Finance, and IT. Oversees people, process, and technology to ensure logical integrity, consistency, and quality.
Thirty years in Tier 1 Scientific Machine Learning Research; publishes on state-of-the-art attention and sequence models, with a proven history of applying them to real world problems.
Transforms messy multi-modal data and process constraints to return models both the board room and the control room can actually trust.
Sits between the executive sponsor and the science — scopes the problem worth solving before a single line of code is written. Every problem worth solving is unique, no solution worth having is precisely identical.
Keeps complex multi-site AI|ML deployments on schedule and the handovers clean, so the work survives after we leave. Combines world class technical expertise with dynamic technical leadership.
Data scientists, data engineers, software engineers, MLOps|DevOps and simulation specialists — staffed to the problem, not to a static bench.
We pride ourselves on having spent more than a decade head hunting AI|ML talent before the current AI bubble emerged. We send true industry seniors to ship world class AI|ML product and service you can trust.
We start from the process model and the instrumentation, the data foundation and the ontology, and only then pick the method. Explainability is a requirement, not a slide.
Documented, monitored, owned by your team. Success is your engineers running it a year later without us. We believe in conservative AI|ML principles of ownership and control. You should never have to settle for anything less.
