For investors & technical leaders
Enterprise software has accumulated a $50B+ graveyard of failed automation — RPA that breaks, AI assistants that augment without eliminating toil, and ERP implementations that created data silos instead of solving them.
Theoremic is not another tool in the stack. We are the reasoning layer above the stack — the autonomous brain that turns passive systems of record into active systems of action.
The market thesis
Enterprise AI has been overhyped and underdelivered for a decade. The infrastructure to change that — agentic orchestration, multimodal reasoning, real-time data unification — has only just matured. The window to build the category-defining layer is now.
Spent annually on enterprise procurement software that still requires humans to manually reconcile data, approve routine transactions, and manage tail spend
Of procurement team time consumed by reconciliation and data entry — not strategic category management
In negotiated enterprise savings that never reach the bottom line annually due to contract leakage, invoice mismatches, and unmanaged spend
Market opportunity
We are entering through the highest-pain, most measurable function in the enterprise — then expanding the intelligence layer across every business-critical workflow.
Beachhead
Tail spend management, intake automation, and contract compliance — the immediate addressable market for Claro, our flagship product.
Platform expansion
Finance, supply chain, HR, legal — every function with structured data and repetitive high-volume workflows is a target for the Platform Core layer.
Long-term vision
The total value at stake when autonomous agents own end-to-end workflows across every major enterprise function globally.
Technical architecture
Theoremic connects via standard enterprise APIs and Model Context Protocol (MCP). No rip-and-replace. No migration risk. No 18-month implementation cycles.
Why Theoremic wins
These are not features. They are structural advantages that compound with every deployment.
Every Cognitive Kernel built for one product instantly upgrades every other product. As we expand vertically, the intelligence layer becomes richer and more defensible with each deployment — not just more expensive to build.
The more contracts, invoices, and sourcing events the platform processes, the more accurate its reasoning becomes. This behavioral data does not transfer to a competitor — it is embedded in the customer relationship.
Once Theoremic is connected via MCP to a customer's SAP and Oracle environment, switching is not a product decision — it is an infrastructure project. The integration depth creates durable retention.
Full audit trails, explainable agent decisions, and human-in-the-loop controls are not add-ons — they are foundational. This makes Theoremic deployable in financial services, pharma, and government where competitors cannot go.
The founding team
We didn't start Theoremic to build another tool. We started it because we had both seen — from opposite angles — why enterprise AI keeps failing to deliver.
The Strategist
He has spent his career on both sides of enterprise AI — close enough to the technology to build it, and long enough inside the strategy to see, at scale, why it so often fails to deliver. Across 15+ years advising many of the world's largest enterprises, one conviction hardened: the enterprises that win with AI won't be the ones that adopt it fastest, but the ones that adopt it in a way they can stand behind, and trust with real money and real judgment.
He built Theoremic to be that system. Not another tool that tells enterprises where value leaked, but an intelligence layer that acts to keep it — governed, accountable, and built to last. Theoremic is the platform he spent a career wishing his clients had.
Co-Founder — Chief Architect of Value
The Scientist
He has spent his career at the frontier of applied AI — a researcher by training and, today, a builder of enterprise-grade AI products inside one of the world's largest technology companies. Where most of the field chases more capable models, one conviction has guided his work: in the enterprise, the model was never the hard part. Trust is. An answer no one can audit, explain, or reproduce is worth nothing to a company with real money on the line.
He brings that rigor to Theoremic's cognitive kernels — the precision-engineered reasoning layer that turns a capable model into a system an enterprise can actually deploy: explainable, auditable, and built to scale.
Co-Founder — Chief Architect of Systems
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