Implementation Services for Azure AI Foundry: Innovate with Azure AI Apps and Agents

Built for the CTO or head of engineering whose teams have four AI prototypes running on four different sets of keys, no evaluation harness, and no path to production that security would sign. This engagement will accelerate the path from AI prototype to production and eliminate the ungoverned key sprawl that blocks it.

Who this is for

Engineering organizations that have moved past the demo stage and have several AI workloads that need somewhere real to run. The CTO or head of engineering owns it. The blocker is usually not model quality: each prototype was built independently with its own keys and its own data path, and nothing about that arrangement passes a security review or a compliance audit, or scales to production traffic.

What we deliver

• Azure AI Foundry hub and project topology designed around your team and environment boundaries, with managed identity throughout so API keys stop being the access mechanism.
• Network and data isolation: private endpoints, managed virtual network, customer-managed keys where the data classification requires it, and a documented data flow per workload that a security reviewer can sign.
• Model deployment and quota strategy: provisioned throughput versus pay-as-you-go, regional placement against capacity availability, and a fallback design so a quota limit degrades, not fails.
• An evaluation and safety harness: automated evaluation runs against a curated test set, Azure AI Content Safety configured per workload, tracing through Azure Monitor, and a promotion gate every workload must pass before production.

Outcomes our customers see

First production workload live 45 days from engagement start at a customer that had spent 7 months unable to move a prototype past security review. Managed identity migration eliminated 100 percent of the 34 API keys previously in circulation across development teams. Provisioned throughput and fallback design held one customer's AI service availability above 99.9 percent through a regional capacity constraint that took their previous setup offline for 6 hours.

How this compares

Most Azure AI engagements build one workload well and leave the platform question unanswered, so the second and third workloads repeat the security conversation. This one builds the platform first, with the evaluation harness and promotion gate as first-class deliverables, because what blocks AI in enterprises is not model capability but the absence of a repeatable path to production.

Architecture and Microsoft alignment

The platform is Azure AI Foundry with a hub and project topology, connected to Azure OpenAI and other model deployments, with Azure AI Search for retrieval where a workload requires grounding. Networking uses managed virtual network with private endpoints to storage, key vault, and search. Identity is Entra ID managed identity end to end. Azure AI Content Safety is enforced per project. Observability runs through Azure Monitor and Application Insights with tracing, and Defender for Cloud provides AI workload posture and threat protection. Infrastructure deploys as Bicep, so environments are reproducible. Aligned to the Microsoft solution plays Innovate with Azure AI Apps and Agents and Protect Cloud AI Platform and Apps.

Plans

Plans, prices, and full scope per plan are on the Plans tab of this listing.

Prerequisites

An Azure subscription with sufficient quota in the target regions, Owner or Contributor plus User Access Administrator rights on the target resource groups, and a network design decision from your platform team on whether workloads sit inside an existing hub-and-spoke topology. A named engineering owner per workload is required for the evaluation test sets.

Limitations

This engagement builds the platform and lands the first workloads on it. It does not include application development beyond the integration required to run those workloads on the platform. Model fine-tuning, custom model training, and data engineering pipelines are scoped separately. Azure consumption for models, compute, and storage is billed by Microsoft directly. Regional model availability may constrain the design and is confirmed at scoping.

How to buy

Buy through the Azure portal, using Get it in Azure portal on this listing, so the purchase is billed through your existing Microsoft agreement. Private offers on request.

Next step

Get it now in the Azure portal, or request a private offer if the scope or the price needs adjusting first.