Give every AI use case a foundation it can trust.

At a glance

Best for: Organisations moving beyond isolated proofs or facing fragmented data, model sprawl, integration constraints and unclear production standards.

You leave with: A target architecture, decision principles and phased implementation plan aligned to real use cases and enterprise constraints.

Model-flexible: Architecture should preserve choice where it creates value and standardise where consistency reduces risk.

Architecture layers

• Experience: Applications, interfaces, channels and employee tools
• Agent and intelligence: Models, orchestration, tools, prompts and decision services
• Knowledge and data: Operational data, retrieval, vector stores, analytics and lineage
• Integration: APIs, events, workflow orchestration and system actions
• Control: Identity, permissions, privacy, security, evaluation and audit
• Operations: Deployment, observability, cost, reliability, release and incident management

What this changes for you

• A coherent foundation across multiple AI use cases
• Safer access to enterprise data and actions
• Reduced duplication and unmanaged model proliferation
• Clear technology choices based on use-case requirements
• Better reliability, monitoring, cost visibility and portability
• A phased architecture that supports current value and future change

What you receive

• Use-case, non-functional and constraint assessment
• Current data, application and integration landscape
• Target data and AI architecture
• Model-selection and portability principles
• RAG, agent and orchestration patterns
• Identity, access and data-protection design
• Integration and API architecture
• Evaluation, observability and MLOps requirements
• Cloud, deployment and environment strategy
• Cost, resilience and performance considerations
• Phased implementation roadmap and reference patterns

Process

1. Anchor in use cases: Define the decisions, data, actions, users and risk involved.
2. Map the current landscape: Examine applications, data sources, interfaces, cloud and operating constraints.
3. Set principles: Agree where to standardise, preserve flexibility, isolate risk and reuse capability.
4. Design the layers: Define target patterns for data, models, agents, integration, controls and operations.
5. Validate critical paths: Prove the riskiest performance, security or integration assumptions.
6. Sequence implementation: Prioritise shared foundations without delaying valuable use cases unnecessarily.