The engagement scope states whether implementation and reassessment are included. A review identifies improvements; it does not itself fix the workload or provide a security certification.
For a multi-tenant AI application, we may examine whether customer data boundaries are tested, interrupted jobs can recover, and model costs can be attributed to individual tenants. We also check whether recovery procedures are documented and rehearsed. These are illustrative review areas, not findings about any named customer or product.
Our platform modernization service can help implement agreed improvements. Teams preparing new AI workloads can combine this review with an AI Readiness Assessment. Read our guides to Azure foundations and measuring AI value.
Explore the full Well-Architected Framework Review (WAFR) or discuss your requirements with WTA.
It is an evidence-based inspection of an agreed cloud workload. WTA reviews architecture and operating practices against business requirements, then prioritizes improvements across reliability, security, cost, operations, and performance. The output is a practical decision and remediation plan.
Only when implementation is included in the agreed scope. The assessment identifies and prioritizes changes. Remediation, retesting, and ongoing support can be commissioned separately or included as defined deliverables, with ownership and acceptance criteria agreed before work begins.
Yes. We agree on appropriate access and evidence collection with your team. Architecture documents, configuration exports, monitoring, spending records, and stakeholder discussions can support the review. Production changes and disruptive tests require a separately agreed plan.
A cloud architecture review examines how an existing workload is designed and operated. AI readiness examines whether a particular AI initiative has the business case, data, systems, people, and controls needed to proceed. The assessments can inform one another.
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