Architecture before automation.
The fastest way to create AI value is not always to start building AI.
First understand the process. Then understand the data. Then design the architecture. Then automate.
We use the same disciplined approach whether the solution is a small workflow, an enterprise AI capability or a network of AI agents.
The AI architecture we design
AI sits inside an enterprise architecture. It should not become a disconnected layer of technology.
Our reference architecture connects five areas.
Enterprise systems & data
ERP, CRM, databases, documents, SaaS, APIs, events, data platforms and operational systems.
Integration & orchestration
APIs, events, workflow, retrieval and the orchestration required to connect AI to business processes.
AI & intelligence
LLMs, machine learning models, search, RAG, classifiers, forecasting and decisioning.
Agents & applications
Assistants, agents, workflow automation and applications that deliver the result where work happens.
Governance & operations
Identity, permissions, security, evaluation, audit, observability, cost and operational controls.
The enterprise AI control layer
As AI moves from answering questions to taking actions, organisations need a consistent way to manage it.
We help design controls around every agent.
- Identity
- Permissions
- Data access
- Tools
- Policies
- Human approval
- Audit
- Monitoring
The result is an AI environment in which organisations can understand what their AI systems are doing, what they can access and what actions they are permitted to take.
What we build
Examples of the systems we design and deliver.
Business knowledge assistants
AI grounded in your own documents, policies, products and business knowledge.
AI workflow agents
Agents that perform defined steps within a controlled business process.
Multi-agent workflows
Multiple specialised agents collaborating across research, analysis, drafting, validation and review.
Intelligent document processing
Documents transformed into structured data and automated workflows.
AI decision support
AI that helps teams identify patterns, exceptions, risks and recommended actions.
Reusable AI foundations
Shared architecture, controls and components for organisations moving from individual AI experiments to a scalable AI capability.
Our principles
Six principles that shape every engagement.
Business outcome first
We start with the process or decision, not the technology.
Architecture before product
We design the target state before committing to a platform or model.
Human oversight where it matters
Automation levels are based on business risk, not on what the technology happens to allow.
Least privilege by design
AI agents should have only the data and tools required for their role.
Your environment
We can build inside the cloud and technology estate you already operate.
Production from day one
Security, observability, testing, resilience and operational support are designed into the solution.
From pilot to production.
A prototype can prove that AI is possible.
Production architecture must prove that it is:
That is the difference between an AI demonstration and an AI capability.
See how this would apply to you.
We’ll walk through how the architecture would fit your current systems, data and controls.