Microsoft AI Operations and Governance Implementation

TrellisPoint's Microsoft AI Operations and Governance helps customers extend their use of Microsoft Azure by providing the expertise, operating capability, and governance know-how that most organizations do not have in-house. It is an ongoing operating model that keeps Microsoft Azure AI deployments adopted, governed, and delivering value over time across Azure OpenAI, Microsoft Copilot Studio agents, and Microsoft 365 Copilot workloads built on Azure. How we extend your use of Microsoft Azure We bring the practitioners, the operating model, and the governance framework needed to scale Azure AI beyond initial deployment. Customers use this engagement to:

• Expand Azure AI adoption across more workloads, business units, and use cases without losing control.
• Operate Azure OpenAI, Microsoft Copilot Studio, and Microsoft 365 Copilot at production scale with monitoring, optimization, and value tracking in place.
• Mature Azure AI governance by keeping risk, security, and responsible AI reviews current as the Azure footprint grows.
• Onboard new Azure AI use cases with guardrails already in place, instead of rebuilding the operating model each time.

Expertise, capabilities, and know-how we provide

• Microsoft Azure AI operating model design and execution.
• Azure AI governance, risk, and responsible AI policy development and maintenance.
• Azure OpenAI, Microsoft Copilot Studio, and Microsoft 365 Copilot production operations.
• Azure AI value measurement, adoption analytics, and executive reporting.
• Azure AI roadmap planning and use case intake.

What this engagement manages

• Usage and value monitoring across your Microsoft Azure AI footprint: adoption and outcomes tracked continuously, not just at launch.
• Continuous optimization recommendations for Azure AI workloads as usage patterns emerge, before friction becomes an adoption problem.
• Governance and risk reviews maintained and updated as your Azure AI footprint grows. No surprise exposures, no reactive policy scrambles.
• New Azure AI use case intake and prioritization with guardrails already in place.
• Executive reporting and alignment: leadership gets a regular view of Azure AI performance, risk posture, and business value.
• Azure AI maturity over time. What starts as one process becomes a managed, scalable Azure capability.

How it works

1. Establish the Azure AI Operating Baseline: define success metrics, governance standards, and ownership. Establish what "working" looks like before optimizing for it.
2. Monitor and Optimize Continuously: track usage, value, and friction across your Azure AI workloads. Recommend improvements before small issues become adoption failures.
3. Evolve Azure AI Responsibly: introduce new Microsoft Azure AI use cases with guardrails already in place. Expand deliberately, not reactively.

Engagement model Delivered as an ongoing monthly retainer sized to your Microsoft Azure AI footprint and cadence. Typical range is $5K to $10K per month. Prerequisite Designed for organizations that have already deployed Microsoft Azure AI into production. Assumes a live Azure workload is in place, not a pilot waiting to go live. Why TrellisPoint TrellisPoint has 17+ years of Microsoft implementation experience. We treat Microsoft Azure AI as a business capability that needs an operating owner, not a one-time deployment. Talk to a Microsoft AI Advisor to extend your use of Microsoft Azure AI.