Our mission is to build AI systems that reason adaptively over structured data, honoring real-world rules, constraints, and meaning, so outputs are accurate, interpretable, and reliable in settings where correctness matters.

We exist to help teams and AI systems make better decisions as data, conditions, and context continuously change.

Numeric ML has long relied on 1990s-era approaches optimized for single-task curve‑fitting. Wood Wide AI brings a modern, reusable representation layer to structured data, unlocking interactive numeric reasoning for real-world decision-making.

Wood Wide AI emerged from years of research at Carnegie Mellon University on how to build machine learning systems that remain reliable under real-world complexity, distribution shift, and intervention. Led by Professor Pradeep Ravikumar, whose work spans probabilistic modeling, causality, and structured learning, this research recognized a persistent gap in AI: numeric and tabular data require explicit structure and reasoning, not just statistical pattern matching. Wood Wide translates these neuro-symbolic foundations into a production-ready platform that enables AI systems to reason over numbers with the same rigor that humans apply when making high-stakes decisions.