Transforming Financial Intelligence

Six years of pioneering machine learning solutions that help businesses make smarter financial decisions

Our Story

Back in 2018, three data scientists sat around a coffee shop in Tel Aviv, frustrated with the gap between academic machine learning research and real-world financial applications. We'd spent years watching brilliant algorithms gather dust in research papers while businesses struggled with outdated forecasting methods.

That conversation led to Syntriva. We started with a simple belief — that machine learning could revolutionize how companies understand their financial future, but only if it was accessible, practical, and built by people who actually understood both the technology and the business challenges.

From our first office in Ramat Gan, we began developing algorithms that didn't just predict numbers, but provided actionable insights. Our breakthrough came when we helped a mid-sized manufacturing company reduce their forecasting errors by 47% in the first quarter. Word spread quickly in Israel's tight-knit business community.

Today, we're proud to serve companies across industries, but we haven't forgotten our roots. Every algorithm we develop still gets tested against that original question: "Will this actually help a business make better decisions?"

Mission & Values

We exist to bridge the gap between advanced machine learning and practical business intelligence, making sophisticated financial forecasting accessible to organizations of all sizes.

Precision

Every prediction we generate undergoes rigorous validation. We don't just aim for accuracy — we strive for reliability that businesses can stake their future on. Our models are tested against historical data spanning multiple economic cycles.

Transparency

Black box algorithms have no place in financial decision-making. We provide clear explanations for every prediction, showing you not just what will happen, but why our models believe it will happen.

Adaptability

Markets change, and so do our models. We've built systems that learn continuously, adapting to new patterns and market conditions without losing the stability that businesses require for long-term planning.

Leadership Team

Our team combines deep technical expertise with real-world business experience. We've worked at major financial institutions, tech companies, and research labs, bringing together perspectives that span both theory and practice.

Dr. Michael Chen

Chief Technology Officer

Michael spent eight years developing risk models at major investment banks before joining academia. His PhD research in computational finance has been cited over 200 times, and he holds three patents in algorithmic trading systems.

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David Rodriguez

Head of Machine Learning

David's background spans both Wall Street and Silicon Valley. He led data science teams at two unicorn startups and previously worked as a quantitative researcher at a hedge fund managing B in assets. He's passionate about making complex algorithms understandable.

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