Tecumseh.AI exists because the hardest part of enterprise AI is no longer the model. It is proving the deployment is legitimate — and building it so that it always was.
Data residency rules, privacy law and procurement standards are converging on the same expectation: sensitive information stays where it was created, and every use of it is accountable. That expectation is not a temporary obstacle — it is the shape of the next decade of infrastructure.
Most of the market is optimizing for interface. We are optimizing for deployability: isolation, auditability, and a control plane that travels with the model. It is unglamorous work, and it is the reason our deployments reach production.
Former Senior Advisor to Ontario's Minister of Finance, where he led modernization of the enterprise-wide risk management framework strengthening governance and accountability across all 25 ministries and major public agencies.
Built and managed a $1.5B asset portfolio and originated $3.6B in self-sourced investment opportunities over a 15+ year career in wealth management and corporate banking at Scotiabank and RBC Dominion Securities.
Three decades building and operating large-scale data systems in privacy-regulated environments, hands-on across the stack: secure infrastructure, databases, document workflows and automation.
An operator background — has built and run compliance-driven data businesses end to end, from first architecture through production support.
Our deployments are built on NVIDIA accelerated computing running inside client-controlled environments — a production foundation for sovereign workloads today, with a clear path to scale as model complexity grows.
We are selective about engagements and direct about what is deployable.