About

Domain-specialized AI, built for the work that matters.

Quantimental Technologies builds vertical AI products that are precise for their domain, contextually aware, and engineered for high-stakes professional work.

What we believe

Every workflow has its own nuance and unique requirements.

Frontier AI models are powerful but indifferent to domain. They hallucinate on specifics, miss provenance, and weren't designed for the workflows of specialists. At the other end, incumbents leave market gaps unaddressed as they race to dominate their broader categories.

We build for the gap between those poles. Each Quantimental product is engineered for precision in specialist workflows, priced for accessibility, and designed to scale into clear, monetizable market gaps.

The founder

Behind Quantimental.

Adel Haddadin is a three-time entrepreneur and the Founder of Quantimental Technologies. He blends 22+ years in investment banking, asset management, consulting, and B2B distribution with hands-on ML engineering and quantitative research.

He has led complex financings and M&A from large-cap to lower-mid-market, executing $10.7B+ in transactions across North America, Europe, and the Middle East. He also scaled a B2B wholesaler into one of Ontario's largest specialty-chemicals suppliers, demonstrating the ability to sell into regulated, high-spec environments and navigate long enterprise cycles. Earlier in his career, he held roles at Standard Chartered Bank, Union Bank, Manulife Investment Management, and EY.

Adel partners to identify and prioritize AI and data use cases across strategy, finance, and operations. He delivers NLP/LLM solutions using prompt engineering, retrieval-augmented generation (RAG), and domain adaptation, and applies ML and econometrics to valuation, scenario analysis, and risk assessment. He also designed the end-to-end architecture and authored most of the code for Marginal AI and Garner AI.

Adel is a Chartered Investment Manager. He holds a BA in Economics (Concordia), an MBA (IE Business School), and an MSc in Financial Economics (Birkbeck, University of London). He is a PhD researcher and candidate in Statistics at Queen Mary University of London, focusing on Bayesian and machine-learning methods for financial services and AI.

See what domain-specialized AI looks like in production.

Marginal AI launches early June 2026. The first 200 pre-registrations receive 350 Compute Units free.

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