Rehoboth Group builds decision-grade models — data-driven AI and physics-based alike — and carries them from whiteboard to production for organizations where the answer has to be right.
Most teams do one or the other. Rehoboth Group works fluently across data-driven learning and first-principles physics — and chooses the right tool, or fuses both, for the problem in front of it.
Models that learn from your data — from classical statistical learning to modern generative and agentic systems built for real decisions.
When the system is governed by physical law, we model it directly — with the applied mathematics to make it tractable and accurate.
A model that never ships changes nothing. We engineer for production and rigorously test what we deploy.
Every model rests on explicit assumptions and is validated against ground truth. We tell you where it holds, where it breaks, and how confident you should be — because in high-stakes domains, a model you can't trust is worse than no model at all.
We architect for the day after delivery: documented, reproducible, and deployable in your environment. The goal is capability your team owns and operates — not a dependency on us.
A sample of engagements across defense, industry, insurance, and finance. Sensitive details are generalized.
Architected and deployed a multi-agent LLM system that compressed a four-hour analyst workflow to roughly five minutes for about 1,000 users — serving as principal investigator, technical lead, and lead developer.
Built predictive models that cut claims-handling costs by 45% for an insurance client, and developed DeepCareAI, a cloud-native risk-management platform for the workers' compensation sector.
Applied advanced process control and predictive modeling to prevent unscheduled shutdowns at a major ammonia facility — protecting millions in potential losses through model-predictive control.
Coordinated data-science strategy and applied deep learning and computer vision to Lidar, SAR, panchromatic, and thermal-infrared imagery across multiple analytic branches.
Rehoboth Group is led by Dr. Babatunde Oguntade, a PhD applied scientist who has spent more than fifteen years at the rare intersection of deep mathematical modeling and real-world deployment.
His path runs from computational fluid dynamics and process control in heavy industry, through quantitative finance, to senior AI roles across the federal government — including a White House Presidential Innovation Fellowship and advisory work for defense, intelligence, and homeland-security organizations.
That range is the point. The same person who can derive the governing equations can also stand up the production system and test whether it actually works. When you engage Rehoboth Group, that's who's on the problem.
Rehoboth Group brings its modeling depth to bear inside the domains where it knows the terrain — each practice tuned to the data, constraints, and stakes of its industry.
Squeezing performance and safety out of complex physical plants.
Turning transaction history into pricing and demand decisions.
Systematic strategies built, tested, and put into production.
Machine learning across the insurance value chain.
Whether it's a generative-AI deployment, a physics-based simulation, or a forecasting system you need to trust — tell us what you're trying to decide, and we'll tell you honestly whether and how we can help.
Email Dr. Oguntade