designing banking, health care and other systems that serve millions of users, and solving problems where getting it wrong costs organisations millions. He is one of fewer than 700 Open Group Distinguished Architects worldwide, inventor of three software patents, and an international keynote speaker. GRL spent 3 years in R&D before releasing their GRL Generators. GRL’s slogan is: Define once. Generate forever. You define your business once: your entities, relationships, rules, and regulatory obligations. GRL Generators then produce production-grade APIs, React applications, MCP servers, message queues, and governed AI agents in minutes, not months. Change the definition, and every system regenerates automatically. No re-coding. No integration rework. No compliance gaps. Think of it as a mini Palantir, but AI-native, built on modern open standards, fast to deploy, and without the price tag or lock-in. Where Palantir connects and analyses data across an enterprise, GRL goes further: it generates the entire technology stack from a single business definition, and keeps every system in sync as the business evolves. This solves three of the most expensive problems in enterprise IT. Cost: most enterprises spend 60–70% of their technology budget maintaining and integrating existing systems rather than building new capability. GRL eliminates that by generating systems from declarations rather than hand-coding them. Risk: because every generated system reads from the same business definition, compliance policies are enforced structurally across the entire stack, not bolted on as an afterthought. It also addresses the problem of continued regulation with built-in full auditability and provenance: every data transformation and AI agent decision is traced end-to-end, giving regulators and internal audit a complete, machine-readable chain of evidence. GRL is built for heavily regulated and complex industries. Banking, government, climate change, healthcare where policy and regulatory change is ongoing, the platform propagates updates across every system in minutes rather than months of manual rework. In healthcare, where patient data is locked inside dozens of disconnected systems, GRL integrates clinical, laboratory, pharmacy, and compliance data into a single view, giving clinicians a complete patient picture with full provenance and governed AI access. The GRL Semantic Agent Harness governs AI agent behaviour so that every agent operates within a declared profile specifying what it can access, what it can assert, and what actions it may take. Every output is validated before it reaches any downstream system. This is AI governance by structure, not by chance, and it is what regulated industries need as they move from experimental AI to production deployment. The platform is built on open W3C standards with no vendor lock-in. GRL holds patents and patent pending in AI governance. Dougal was an ISWC 2019 keynote speaker, and is speaking at the Knowledge Graph Conference 2026 (Cornell Tech, New York, May 4–8) on AI safety, semantic AI harnesses, and AI knowledge graph engineering.