Design the AI environment around the business use case.
Connect platforms, data, applications, integrations, cloud and infrastructure into an architecture that fits the use case, risk, economics and operating model.
Start with the decisions that shape the initiative.
Which AI platforms and deployment models fit the business requirement?
How should AI connect to Microsoft 365, CRM, ERP, analytics and line-of-business applications?
What data, integration, identity, cloud, network and infrastructure dependencies matter?
Which capabilities should be operated internally, through existing providers or through managed services?
Structure the work around decisions and outcomes.
Architecture discovery
Document the use case, current platforms, data flows, applications, identities, integration patterns and operating constraints.
Platform & vendor evaluation
Compare native capabilities and credible alternatives on fit, integration, security, economics, complexity and long-term viability.
Target architecture
Define how applications, data, models, agents, APIs, cloud and infrastructure should fit together.
Delivery & operating design
Sequence implementation and determine where internal teams, current providers or specialist delivery capabilities should be used.
Discover. Prioritize. Deliver.
Each engagement follows the same decision path while adapting the scope to the business problem.
Establish context.
Business objective, current state, stakeholders, data, technology, risk, dependencies and existing providers.
Make the decisions clear.
Value, feasibility, risk, control needs, architecture options, sequencing and measures.
Move the priority forward.
Advisory, training, design, implementation planning, specialist delivery and ongoing operations as required.
Clear decisions, documented next steps.
- Current-state and target-state architecture
- Platform and vendor decision criteria
- Data and integration requirements
- Infrastructure / cloud requirements where justified
- Implementation and operating model
Grounded in recognized frameworks and platform guidance.
We use relevant standards to organize risk, controls and evidence while keeping recommendations aligned to the organization and initiative.
Discuss your AI architecture.
We’ll start with the use case and current environment before recommending a platform, architecture or provider.
Get in touch →