The judgment to know what to build, and the engineering to ship it.
Applied AI is my consulting practice for regulated and data-intensive organizations. For over a decade, I have helped teams move from AI ideas and pilots to governed production workflows, combining strategy, architecture, evaluation, and hands-on implementation.
Stefan Jansen, Principal
I work on the strategy, architecture, evaluation, and code. I came to machine learning through quantitative economics, financial markets, and the business side of technology, then added the engineering depth to build the systems myself. That sequence shapes the practice: first establish which decision or workflow should improve, then choose and build the approach that can be evaluated, governed, and operated.
Before founding Applied AI, I created Infusive Intelligence and built its New York office around data science for investment research: quantamental workflows, forecasting, market-event models, and an internal research platform for a public-equities strategy.
My work spans agent workflows, contract intelligence, forecasting, customer-lifecycle systems, and quantitative research infrastructure. I am also the author of Machine Learning for Trading, now in its third edition, and have taught more than 100,000 professionals and learners. I hold a Harvard master's in economics and public policy, a Georgia Tech MS in computer science, and the CFA charter.
Selected Work
My work spans insurance, healthcare and financial markets, including multi-year implementation engagements, advisory work and executive education. Organizations I have worked with include RxSense / SingleCare, iptiQ (Swiss Re's digital insurance business), and Loeb Enterprises and its portfolio companies.
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Contract intelligence in production
Built core components of a contract-intelligence system that reached production and cleared internal governance review. Its architecture is documented in U.S. Patent US12705675B2 and European Patent EP4555470B1.
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Forecasting and customer-lifecycle systems
Forecasting, retention, customer-lifecycle, and campaign-analytics systems for pharmacy-technology and healthcare workflows.
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Customer analytics and data-science capability
Advised portfolio-company teams on machine learning and statistical modeling, with a focus on healthcare and consumer analytics: customer targeting, campaign analysis, model design, and building out in-house data-science capability.
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Live trading infrastructure
Designed, audited, and operated research and execution infrastructure across equities and digital assets, including live systems for a regulated European digital-asset investment firm.
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Executive AI education
Designed and delivered executive education on AI, data science, and analytics for senior leadership.
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Agent and forecasting systems
Agent workflows designed around explicit evaluation, governance, and operating requirements rather than treating the agent as the unit of value.
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Machine Learning for Trading
Author of the ML4T book, now in its third edition, and its open-source ecosystem.