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AI

Microsoft Copilot rollout, custom copilots with Copilot Studio, and Azure OpenAI integrations for the parts of your business where AI actually pays back.

What's included

  • Microsoft 365 Copilot deployment, licensing, and governance
  • Custom copilots built with Copilot Studio for internal workflows
  • Azure OpenAI deployment and integration with line-of-business applications
  • AI-augmented Power Platform automations with AI Builder
  • AI for SharePoint: search, summarization, and knowledge mining
  • Privacy-aware deployments for HIPAA-aligned and other regulated environments
  • AI policy and guardrails before tenant-wide rollout
  • Adoption training so the people who get the licenses actually use them

What we deliver

AI for a mid-market business is mostly not about building a foundation model. It is about getting Microsoft 365 Copilot rolled out cleanly, building two or three custom copilots that automate specific internal flows, and integrating Azure OpenAI into line-of-business applications where it actually saves time. Boring on paper, real impact in practice.

We focus on Microsoft-native AI tooling because that is where most of our clients already have licenses, identity, and data residency. Copilot in Word, Excel, PowerPoint, and Teams. Copilot Studio for the custom copilots. Azure OpenAI for the heavy integration work. Power Automate with AI Builder where the workflow already lives in the Power Platform.

Where we typically start

Most engagements begin with a 2 to 4 week AI readiness assessment:

  • License audit. Microsoft 365 Copilot is per-user and not cheap. We help right-size who actually needs it versus who could be served by a cheaper alternative or a custom solution.
  • Data readiness. Copilot is only as useful as the SharePoint information architecture behind it. If your IA is a mess, Copilot’s answers will be a mess. We tell you straight which IA work needs to happen first.
  • Governance. Sensitivity labels, conditional access for AI features, oversharing audits, retention policies. Copilot can accidentally surface documents nobody remembered were oversharing.
  • Use-case prioritization. Two or three workflows where AI demonstrably saves time, with measurable success criteria. Not ten use cases that all stall at the pilot phase.

You get a written report with prioritized recommendations and a fixed-fee proposal for the work.

Common engagements

Microsoft 365 Copilot rollout. Pilot with 25 to 50 users, measure adoption and time saved, expand or retire the licenses based on what the data says. We have written about the rollout playbook in detail for clients who want to start planning before talking to us.

Custom copilots with Copilot Studio. When the out-of-box Copilot does not fit a specific internal process. We build copilots that pull from SharePoint, integrate with Dynamics or third-party APIs, and run inside Teams where the team already lives. Citizen-developer-friendly to maintain after we hand off.

Azure OpenAI integrations. When the AI work needs to live inside a custom application. Retrieval-augmented generation against client data, embedded in a .NET or web application, deployed to Azure where the rest of your stack already runs. We have written about picking between Copilot Studio and Azure OpenAI for the decision-making before you start building.

AI policy and guardrails. The work that has to happen before you let AI loose on a tenant. Acceptable-use policies, prompt-injection awareness training for power users, data classification reviews, audit logging configurations. Boring, necessary, and the part most rollouts skip and regret.

Who we work with best

The clients who get the most out of AI engagements with us are the ones whose Microsoft 365 environment is already in reasonable shape, who can name a specific workflow they want to make faster, and who have one executive willing to sponsor adoption rather than treat AI as an experiment somebody else owns.

We are less useful for organizations that want a “transformation” with no specific use cases attached, or for teams whose underlying IA, identity, and data hygiene need a year of work before any AI tool can produce reliable answers.

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