Building the infrastructure for trustworthy AI reasoning—from one business architect's frustration with manual processes, to a governed semantic reasoning platform used by institutional investors.
Three interconnected innovations that make governed AI reasoning possible.
Orthogramic began with a fundamental frustration: business architecture was trapped in manual processes and disconnected tools. Despite decades of frameworks like TOGAF and BIZBOK, practitioners spent more time wrestling with documentation than generating insights.
The breakthrough came from recognising that business architecture needed what software had long possessed—a schema-first approach with machine-readable representations. The Orthogramic Metamodel emerged as a universal grammar for enterprises: 24 interconnected domains covering everything from Strategy and Capabilities to Information and Technology.
By defining every domain through JSON Schemas with explicit attributes, relationships, and cross-domain links, we created something unprecedented—a semantic foundation that enables automation, validation, and consistent interpretation across any organisation. We open-sourced it under CC BY-SA 4.0 because we believe a universal business grammar should be accessible to all.
Open Source — CC BY-SA 4.0With the metamodel providing semantic structure, we turned to a harder problem: how do you govern AI reasoning itself? Large language models are powerful but probabilistic—they can generate plausible-sounding content that lacks evidentiary grounding or violates organisational constraints.
The Reasoning Fabric was our answer. Rather than treating AI as a black box that produces outputs to be reviewed, we built a control plane that governs the reasoning process from intent to insight. The fabric transforms raw inputs into structured representations aligned with the metamodel, enforces configurable rules and constraints throughout, tracks evidence and confidence for every claim, and maintains complete provenance of all transformations.
This isn't governance applied as an afterthought—it's governance embedded in the architecture. You can't bypass what's structurally enforced. The result: institutional-quality deliverables where every conclusion traces to its sources and every inference operates within defined boundaries.
Patent PendingFor many applications, software-based governance is sufficient. But for safety-critical environments—financial regulation, infrastructure decisions, scenarios where trust cannot be assumed—we needed something stronger.
The HGCP represents a new approach to AI infrastructure: governance guarantees embedded at the infrastructure level itself. Unlike software-only approaches that can potentially be circumvented, HGCP creates strong isolation between governance mechanisms and governed components.
The architecture provides continuous AI state monitoring to detect policy deviations before outputs are released, protected execution environments where governance operations cannot be bypassed, verified output gating that ensures only compliant results reach their destinations, and tamper-evident audit infrastructure for regulatory compliance and forensic analysis. For organisations where the stakes are highest, HGCP offers what software alone cannot: non-bypassable, hardware-enforced constraint compliance with complete audit trails.
Patent Pending — Enterprise ReadyFounder & Business Architect
Damian brings over 25 years of experience spanning the full spectrum of enterprise technology—from infrastructure to strategy, from hands-on systems administration to AI-powered business intelligence.
His career began in the trenches: HP-UX Unix system administration at Workcover Queensland, managing core systems during the era of mainframe-to-Oracle migrations. This foundational experience in how enterprise systems actually work—the hardware, the databases, the integration patterns—would prove invaluable decades later when designing infrastructure-level AI governance.
From there, Damian's trajectory moved steadily toward the intersection of technology and business strategy. He progressed through technical business analysis, solution architecture, and eventually business architecture—working across government, financial services, healthcare, education, and media sectors. Along the way, he founded multiple successful startups, managed complex enterprise transformations, and developed deep expertise in CRM implementations, data platforms, and digital transformation.
Orthogramic emerged from a simple observation: despite working with sophisticated organisations and talented teams, he was doing the same manual business architecture work over and over. The metamodel started as an attempt to codify best practices; the reasoning fabric evolved when he saw what LLMs could do—and more importantly, what they couldn't do without governance.
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