Company

Applied AI leadership for enterprise decision-making

OKEMA was founded on a simple conviction: enterprises rarely struggle because they lack data. They struggle because they lack systems that consistently recommend the best decision.

OK

The OKEMA Team

Applied AI, ML engineering, and causal inference for the enterprise

Builders of Vitallogix, a production ops platform in daily use
Decision intelligence for insurers, banks, and telecoms
Causal inference, ML, and reinforcement learning in production
Applied-AI experience from Microsoft, Netflix, and DoorDash
Why this company exists

After years building production AI systems at companies like Microsoft and conducting research in causal inference, reinforcement learning, and recommender systems, one pattern kept repeating: organizations had the data, the dashboards, and the models, but the decisions still came down to intuition, manual processes, and static reports.

A churn score doesn't save a customer. A fraud flag doesn't close a case. A forecast doesn't set a price. Value is created at the moment of the decision, and that moment was being left to chance.

"Predictions don't create value. Decisions do. OKEMA exists to build that decision layer."

We build, we don't just advise. The same team built Vitallogix, a platform a home-health agency uses daily to digitize records, automate data entry, and QA billing and payroll documents, cutting that review from eight hours to two with fewer errors. OKEMA brings the same event-based data and decision-automation approach to insurers, banks, and telecoms.

OKEMA helps insurers, banks, and telecom operators move from data visibility to decision intelligence, turning the systems they already run into ones that recommend, act, and learn. We're early, and we'd rather earn proof than overclaim it. We start with a focused roadmap and grow into the decision layer for the enterprise.

Experience

Decision systems, built and shipped

8+ years of applied AI and ML across causal inference, reinforcement learning, recommender systems, predictive and prescriptive modeling, and responsible AI, deployed into real products at enterprise scale.

Causal Inference Reinforcement Learning Recommender Systems Contextual Bandits Responsible AI Prescriptive Modeling Counterfactual Systems
$808K
Lost LTV captured via treatment-effect modeling
$5.7M
LTV improvement from behavior + causal models
Named inventor on two issued machine-learning patents from work at Microsoft, with publications in causal models, contextual bandits, health interventions, and counterfactual systems.
Microsoft
Applied Scientist · Tech Lead

Built and deployed ML systems into products and led two issued machine-learning patents; LTV impact via causal and language models.

Netflix
Applied ML

Recommender systems and experimentation at consumer scale.

DoorDash
Applied ML

Production ML pipelines for marketplace decisions.

UVA
PhD Research

Causal inference, contextual bandits, and health-intervention systems.

Reflects the team's prior employment and research, not OKEMA customer relationships.

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