Enterprise AI · Shared production platform capabilities
Production AI Foundation.
Build the cloud and delivery foundation that lets selected AI workloads use approved models and data with clear security and operating controls.
- Indicative timeline
- 4–8 weeks for a defined foundation and representative workload validation. Security approvals, data access and integration count affect delivery time.
- Investment approach
- Quoted after agreeing workload boundaries, integrations, security controls, evaluation requirements and operational scope. Cloud resources, model consumption and licenses are separate.
01 · Business challenge
The problem this package addresses
AI workloads add requirements to an existing cloud platform: model access, data boundaries, evaluation, agent permissions, observability and variable inference costs. This foundation builds the shared controls for an agreed workload class before multiple teams depend on them.
02 · What you get
Defined technical outcomes
- A reference architecture and decision record for the selected AI workload class.
- Infrastructure and deployment configuration for agreed identity, network, policy and model-access boundaries.
- A repeatable release and evaluation path with selected guardrails and operational telemetry.
- Cost visibility, runbooks and a handover plan for the platform and workload owners.
03 · Scope of work
Work included
- Define the AI workload boundary, users, model and data access patterns, security expectations and ownership.
- Implement agreed cloud resources and connectivity for model and application access, using managed identity and least privilege where applicable.
- Add CI/CD and selected evaluation, safety, telemetry and cost controls to the release path.
- Validate the foundation with a representative workload or integration and document how teams request, deploy and operate it.
04 · Acceptance
How completion is reviewed
Final acceptance checks are agreed in the statement of work before implementation. Typical checks include:
- The agreed foundation deploys through its documented IaC and release workflow.
- Identity, network, model access and telemetry checks pass for the representative workload boundary.
- Evaluation and release gates are demonstrated against the agreed checks, with limitations documented.
- Platform and workload owners receive runbooks, decision records and handover guidance.
05 · Delivery process
From discovery to handover
- DiscoverConfirm the target users, workload boundary, dependencies and acceptance conditions.
- DesignAgree the architecture decisions, delivery sequence, access model and implementation plan.
- Build and validateImplement the scoped work in the client environment and review it against the agreed checks.
- Hand overTransfer code, documentation, runbooks and ownership to the client team.
06 · Technical foundation
Tools and architecture
Cloud AI model access across Azure, AWS and Google Cloud; cloud identity and workload permissions; private networking; governance policies; secrets management; RAG or bounded agent integration; evaluation, CI/CD, observability, guardrails and AI FinOps controls.