A trillion-dollar gap
between promise
and reality.

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.

$50B+

Spent annually on enterprise procurement software that still requires humans to manually reconcile data, approve routine transactions, and manage tail spend

60%

Of procurement team time consumed by reconciliation and data entry — not strategic category management

$1T+

In negotiated enterprise savings that never reach the bottom line annually due to contract leakage, invoice mismatches, and unmanaged spend

Market opportunity

Procurement is the entry point.
The platform is the prize.

We are entering through the highest-pain, most measurable function in the enterprise — then expanding the intelligence layer across every business-critical workflow.

Beachhead

$12B

Procurement automation

Tail spend management, intake automation, and contract compliance — the immediate addressable market for Claro, our flagship product.

Platform expansion

$85B

Enterprise agentic AI

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

$300B+

Autonomous enterprise

The total value at stake when autonomous agents own end-to-end workflows across every major enterprise function globally.

Technical architecture

Built to sit above,
not replace.

Theoremic connects via standard enterprise APIs and Model Context Protocol (MCP). No rip-and-replace. No migration risk. No 18-month implementation cycles.

Products
Claro — Spend Control Dormio — Auto-Renewal Unio — Unified Sourcing Lumen — Contract-to-Project Cipher — Contract Intelligence Forma — Standardisation
Domain-specific agents, each composed of Cognitive Kernels
Cognitive kernels
Contract Parser Invoice Auditor Negotiation Engine Intake Router Policy Enforcer
Reusable, specialized AI logic units — the atomic building blocks
Theoremic Core
Data Spine Orchestration Engine Governance Layer Human-in-the-Loop Audit Trail
The autonomous brain — reasoning, routing, and controlling agent workflows
Integrations (MCP)
SAP Oracle Workday Coupa Ariba Slack / Teams Email / PDFs
Your existing stack, untouched — we read and act through standard APIs

Why Theoremic wins

The defensible advantages.

These are not features. They are structural advantages that compound with every deployment.

01

Kernel reusability creates compounding returns

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.

02

Enterprise data becomes a proprietary asset

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.

03

MCP integration is a lock-in layer, not a feature

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.

04

Governance-first architecture wins regulated industries

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.

Product roadmap

Building the autonomous enterprise,
one deployment at a time.

Now — Q3 2026

Claro — Intelligent Spend Control

The Auditor, Concierge, and Value Creator Kernels deployed as an integrated spend management product. Q3 pilot cohort currently forming with selected enterprise partners.

Deploying now

H2 2026

Dormio — Auto-Renewal Intelligence

Continuous monitoring of every auto-renewal and evergreen clause across the full contract estate. Calculates precise renegotiation windows and triggers stakeholder workflows before the renewal date decides for you.

Building

H2 2026

Cipher — Contract Intelligence for Complex Agreements

Purpose-built for EPC contracts, framework arrangements, and multi-party structures. Generates role-specific intelligence — finance, engineering, HR, legal each receive exactly what they need from the same document.

Building

H1 2027

Unio — Unified Sourcing Intelligence

A single policy-aware sourcing layer across every business unit, geography, and category. Surfaces preferred suppliers and negotiated pricing at the point of need — closing the gap between what procurement negotiates and what the business actually spends.

Planned

H1 2027

Lumen — Contract-to-Project Intelligence

Continuously maps actual project status against contractual commitments — identifying scope creep, milestone drift, and vendor performance gaps before they compound into disputes.

Planned

H2 2027

Forma — Contract Standardisation Intelligence

Analyses the entire contract estate, surfaces template variation by category and risk profile, and recommends consolidated standards based on actual practice. Monitors new agreements continuously against approved templates.

Planned

2027+

Theoremic Core — horizontal expansion

Extension of the reasoning layer beyond procurement to finance, legal, and HR workflows — leveraging the same Kernel architecture and ERP integrations built for the source-to-pay suite.

Vision

Built by people who have
seen both sides of
the failure.

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 — Co-Founder

"Having advised globally leading enterprises on AI transformations, I saw the trillion-dollar gap between hype and actual business value. We built this company to finally bridge that gap."

AI strategy & enterprise GTM background

The Scientist — Co-Founder

"Enterprise-grade AI requires more than a model. It requires a precision-engineered orchestration layer. Drawing on years building core AI infrastructure in Big Tech, we've designed Theoremic for explainability and scale."

AI infrastructure & Big Tech engineering background

Ready to see the platform in depth?

Request the full investor deck — including detailed architecture diagrams, unit economics model, and competitive landscape analysis.

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