Cloud AI Modernization
Bring production AI to a multi-cloud estate — AWS, Azure, GCP — without a rip-and-replace.
The estate that has to run AI workloads was designed before them. Provisioning takes weeks, environments drift because they were built by hand, identity is inconsistent across providers, and the platform team is already fully committed to keeping what exists running.
The instinct is a new greenfield platform. In practice that produces a second estate to operate alongside the first, and the migration that was supposed to follow never gets funded.
This engagement modernises what you have toward AI workloads incrementally, sequenced so each step is independently useful.
What the engagement includes
Estate assessment
What your current platform can carry, where it blocks AI workloads, and which constraints are genuine versus habitual.
Terraform-first foundations
Environments as reproducible infrastructure code rather than hand-built, so provisioning stops being the bottleneck and drift stops being routine.
Kubernetes as the scaling layer
Container platform patterns for AI workloads — GPU scheduling where relevant, autoscaling, and workload isolation.
Consistent identity across clouds
Workload identity federation so services and agents authenticate consistently across AWS, Azure, and GCP instead of per-provider credential sprawl.
Incremental migration plan
Sequenced so each step delivers value independently and can be stopped without leaving a half-migrated estate.
Who this is for
- Multi-cloud estates where provisioning blocks AI delivery
- Platform teams asked to support AI workloads with existing capacity
- Organisations with GovCloud or Azure Government requirements
When this is the wrong engagement: If there is no existing estate to modernise, this is the wrong shape — enterprise AI architecture builds the platform directly.
How it runs
- Discovery (30 minutes). A working call to map your stack, constraints, and the highest-value first step. No pitch deck.
- Scoped fixed-price proposal. A written statement of work with deliverables, milestones, and a fixed price — not an open-ended time-and-materials meter.
- Delivery. Senior architects execute against milestones, with governance applied to every AI-agent action from day one.
- Operate or hand off. Monitored operation, or a clean handoff with runbooks so your team can run it without us.
Questions we get asked
Do we have to consolidate onto one cloud?
No, and consolidation is rarely worth its cost purely to enable AI. Multi-cloud is treated as a constraint to design for rather than a problem to eliminate: consistent identity, consistent infrastructure-as-code patterns, and workload placement decided by data gravity and compliance rather than preference. Consolidation is recommended only where a specific requirement genuinely demands it.
How disruptive is this to teams currently shipping?
The sequencing is designed around that constraint, because a modernisation that halts delivery gets cancelled. Foundations go in alongside existing infrastructure, workloads move when their target is ready, and each step is independently valuable so the programme survives a change in priorities. We will tell you which steps genuinely require a freeze, and they are few.
Do you work in GovCloud and Azure Government?
Yes. Regulated and government cloud environments have materially different service availability, identity federation behaviour, and compliance expectations, and an architecture that assumes commercial-region parity breaks on contact with them. Those differences are designed for explicitly rather than discovered during implementation.
What if our platform team has no spare capacity?
That is the normal starting condition and it shapes the engagement. Work is scoped so Citadel delivers the foundations while your team continues operating, with handoff and runbooks built in from the start rather than assembled at the end. The objective is that your team can operate what is delivered without us, which is also what makes the engagement finishable.
Related services
Enterprise AI Architecture
Reference architectures for agents, RAG, and MLOps that hold up under real load, security review, and audit.
LLMOps
CI/CD, evaluation, observability, and cost control so models ship and stay reliable in production.
Enterprise AI Strategy
A board-ready roadmap from where you are today to governed AI in production — sequenced by value and risk.
Start with a 30-minute discovery call
No pitch deck. We map your constraints and tell you the highest-value first step — including when that step is not an engagement with us.
Book a discovery call