Semantic AI Platform for Enterprise Knowledge Graphs.
Define your business once. Generate everything.
Presenting "AI-Assisted Ontology Engineering: Guardrails and Workflows for Using LLMs Safely".
Your biggest technology cost is not building. It's maintaining and integrating.
Graph Research Labs was founded on a simple truth: organisations should not have to rebuild their technology stack every time their business model or regulations change. Most enterprises spend 60-70% of their technology budget maintaining legacy systems, leaving just 30-40% for innovation. We believe that's wrong and have created a platform with a simple principle: define your business domain once — your policies, rules, and data structures — and our technology can generate every data asset around it. When business or regulations change, you update the definition. Every system, every agent, every report regenerates the data assets automatically.
Our platform sits at the intersection of AI, business logic and data architecture, generating everything from APIs to governed AI agents, and maintaining consistency, provenance and auditability across your entire stack as the business evolves.
Three minutes to understand what GRL does and why it matters.
A high-level walkthrough of the platform — the enterprise pain points it solves, how declarative generation works, and what it means for your architecture, your teams, and your budget.
Platform Overview
You define your business. Our platform generates the technology.
Define your domain - your entities, relationships, and rules - and our platform generates your entire enterprise stack. Change the definition, and every system regenerates automatically. New regulations, new markets, new products, mergers and acquisitions, handled in minutes, not months.
One definition. Everything generated.
From a single business ontology, our platform generates and keeps in sync every layer of your enterprise stack. No manual re-integration. No broken contracts. No versioning chaos.
We handle the complexity. You see results in weeks.
Our team works alongside yours to define, build, and deploy ontology-driven systems. Every engagement delivers working software — not slide decks, not roadmaps, working systems you can test with your own data.
Define
We work with you to model your business domain as an ontology — the single source of truth that drives every system we generate. If you already have an ontology, we can work with that too.
Generate
Our declarative generation engine reads the ontology and produces your enterprise stack — APIs, applications, AI agents, data products — in minutes. Connect your existing data sources without replacing them.
Evolve
When the business changes, update the ontology. Every generated system rebuilds itself automatically. New regulations, new products, new markets — handled in minutes, not months.
Advisory that builds. Data assets you keep.
Built for regulated, complex enterprises
Ontology-driven systems are particularly valuable in industries where compliance, data governance, and cross-system integration are non-negotiable. We work across sectors where the stakes are highest.
What becomes possible when the entire health system runs from one definition.
Today, healthcare data is locked inside dozens of disconnected systems - patient records in one, lab results in another, compliance policies in a third, clinical notes in PDF folders nobody can search. When a regulation changes, every system needs manual updating. When a clinician needs a complete patient picture, they piece it together by hand. GRL changes that.
Define the health system ontology
Patients, clinicians, conditions, medications, compliance requirements, reporting obligations - defined once, precisely, in a way every system can read. GRL Generators produce the APIs, applications, and data products automatically.
Connect legacy systems without replacing them
The knowledge graph integrates data from existing patient management systems, laboratory platforms, pharmacy records, and clinical notes - including unstructured data like PDFs and scanned documents. No rip-and-replace. The legacy systems keep running.
Clinicians see the full picture for the first time
A generated application gives clinicians a unified view of each patient - conditions, medications, lab results, clinical history - drawn from every connected system in real time. Governed AI agents can query clinical notes and surface relevant information, controlled by the ontology.
Compliance reporting generates itself
Regulatory reporting - patient safety metrics, clinical audit trails, funding compliance - is generated directly from the knowledge graph. When reporting requirements change, the ontology updates and every report rebuilds. No re-coding. No compliance gaps.
A regulation changes. The system adapts in minutes.
A new patient data privacy requirement is introduced. The ontology is updated. Every API, every application, every AI agent, every report that touches patient data automatically reflects the new rules. What would have taken months of manual rework across dozens of systems happens in minutes - governed, auditable, and consistent.
Without GRL
- 12–18 months to integrate three legacy systems
- Manual compliance updates across every application
- Clinicians still copying data between screens
- Regulatory changes take months to flow through
- Each new requirement is a new project
With GRL
- Integrated in weeks, not years
- Compliance reporting generated automatically
- One patient, one screen, one truth
- Regulatory changes flow through in minutes
- Each new requirement is an ontology update, not a programme
This started as a prediction in an aerospace boardroom.
In 2014, Dougal Watt was IBM's Chief Technologist and their global expert in information architecture. IBM brought him into Meggitt, a UK aeronautical company, to guide their thinking on how to apply information architecture to advanced manufacturing. During that engagement, Dougal made a prediction that the room struggled to accept: that one day, an ontology would drive entire factories and supply chains. Not guide them. Drive them.
The room was full of exceptional engineers and technologists - the kind of people who build systems that keep aircraft in the sky. What made it hard to accept was that in 2014, nothing like this idea existed anywhere in the world.
Dougal never let go of that vision. After leaving IBM, he founded Graph Research Labs as a research lab with a single purpose: work out how to build it. It took years of research, hard work, and a great deal of persistence to create the declarative generation engine that powers the technology you see on this platform. What was once a prediction in a room full of aerospace engineers, is now working technology - generating complete data access code from a single business definition in minutes.