Who are we and why we’re here.
Viridium AI was built by operators who’ve scaled AI inside real manufacturing environments. That experience shaped a clear point of view: data must be accurate, verifiable, and decision-ready for the hardest use cases. Our team has spent years building systems of record, data platforms, and production AI that enterprises rely on.
That experience comes from shipping and scaling platforms at companies like Microsoft, AWS, Oracle, and Salesforce. Today, we’re applying those hard-won lessons to one of manufacturing’s hardest problems: turning disconnected product, material, and chemical data into decision-ready intelligence teams can actually use.
Our Values
We earn trust through accountability, empathy, quality, and responsiveness. We make AI more accessible, safe, and useful by building reliable, traceable Material Intelligence and clear outcomes for customers. We show up for each other professionally and personally, creating an environment where everyone can do their best work.
We earn trust through accountability, empathy, quality, and responsiveness. We make AI more accessible, safe, and useful by building reliable, traceable Material Intelligence and clear outcomes for customers. We show up for each other professionally and personally, creating an environment where everyone can do their best work.
We deeply understand our customers’ goals and relentlessly drive real business outcomes, not just technical outputs. Every team member engages with customers, learning directly from their challenges and working until issues are resolved. We measure success by the impact we enable, not by internal milestones.
We know we don’t have the luxury of patience. We play to win. We care deeply about product quality and act with urgency. When something isn’t right, we fix it quickly. When we fail, we discuss it openly and without blame so we learn faster and succeed the next time
We hold ourselves accountable for how AI is built, deployed, and trusted. Our platform is grounded in physics-based reasoning and finance-grade data engineering, ensuring insights are traceable, explainable, and defensible. When uncertainty arises, we intentionally keep humans in the loop to review, validate, and correct outputs. Accountability does not stop at automation.