AI Solution Architecture
For enterprise landscapes with more than one AI use case, more than one model, and more than one regulatory frame. We deliver a concrete layer above the builds: agent topology, model choice, hosting, integration map, risk register, AI Act classification, and data sovereignty. Fifteen years of integration experience sit under the hood. Not generic IT architecture.
Who it is for
Enterprise IT directors who own their organisation's AI portfolio. Public and semi-public organisations with multiple AI use cases in the pipeline. Clients who tried one AI pilot, saw it stall, and realised there was no coherent design underneath.
- Enterprise IT directors
- Public and semi-public organisations
- Clients with multiple AI use cases and no coherent design
Problem it solves
Organisations with more than one AI use case end up with a fragmented mess: three vendors, five models, two unrelated agent frameworks, no one who owns sovereignty or AI Act classification, and nothing operating consistently. Integratio designs the layer above the individual builds.
- "We have three AI vendors and nobody has oversight."
- "Our AI pilot stalled. We need to step back and design."
- "Who owns AI Act classification in our organisation?"
What a typical engagement looks like
| Phase | Duration | Output |
|---|---|---|
| Kick-off and scoping | 1 week | Engagement scope: use cases, artefacts, stakeholders |
| Discovery | 2 to 3 weeks | Interviews (IT, security, legal, operations), current-state map |
| Design | 3 to 5 weeks | Agent topology, model + hosting matrix, integration map, risk register, AI Act classification, data sovereignty map |
| Handover | 1 week | Workshop, documentation, decision log |
Team: An AI solution architect as lead, with specialists from our team for governance, integration, and infrastructure.
Concrete AI-specific outputs
- Agent topology: which agents, what each can and cannot do, how they coordinate.
- Model + hosting matrix: per use case, which model, which hosting, and why.
- Integration map: where each agent reads and writes, which events trigger what.
- AI risk register: per use case, risks, mitigations, owners.
- AI Act classification: reasoned, suitable for Legal, DPO, regulator.
- Data flow and sovereignty: where data originates, rests, and inference runs.
- Evaluation framework: how each agent is tested, and how regressions are caught.
Proof
TODO(sanne): name at least one delivered architecture engagement with artefact summary.
What this service is not
- Not a generic enterprise-architecture engagement that happens to mention AI.
- Not a cloud-migration assessment.
- Not a TOGAF exercise.
- Not an MLOps platform build (Managed AI Operations handles the run phase).
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