Practice area 02

Microsoft AI agents built to fit your enterprise — not break it.

From research agents that compile weekly reporting to Copilot extensions inside D365 — built on Azure AI Foundry, MS Agent Framework, and MCP so your agents are maintainable, observable, and actually used.

The problem we solve

Recognize your situation?

AI pilots that never reach production

Proof-of-concepts that demo well but never ship because nobody thought about security, observability, or integration. We build for production from day one.

No clear path to integrate AI into existing systems

Microsoft 365 and D365 are your system of record; you want agents that work inside them, not bolted on. We build on Copilot, MS Agent Framework, and MCP for that reason.

Manual work no one has time to fix

Reconciliation, weekly reporting, Tier-1 support triage — work AI can absorb if scoped correctly. We identify the right first use case, not just any use case.

What's included

What an engagement covers

What we build on Microsoft's AI stack — engineered for production, not just demos.

  • AI research agents and automated reporting
  • Conversational AI and workflow automation
  • Voice agent orchestration and multimodal AI
  • Prompt engineering and reusable prompt libraries
  • MS Agent Framework, Copilot, and Azure AI Foundry agent builds
  • Model Context Protocol (MCP) server setup and integration
  • AI model deployment on Azure

Related use cases

If this sounds like you

“Our finance team is drowning in manual work”

Automate reconciliation, reporting, and approvals with AI agents on top of your existing stack.

“We've been told to 'do AI' but don't know where to start”

Honest readiness assessment, prioritized roadmap, and a quick-win pilot.

FAQ

Questions about this work

How do AI agents actually fit our D365 environment? Will they break things?
Agents read and act through defined APIs and MCP servers — they don't modify your D365 configuration directly. We scope permissions, observability, and rollback paths before any agent touches production data. The chat assistant on this site is a live example of the same approach.
Microsoft-only, or open source too?
This pillar is Microsoft-native. If your problem is better served by open-source or local LLMs (privacy, cost, or compliance drivers), see our Open & Local AI pillar — we'll recommend the right fit, not push one over the other.
Can we start with a small pilot before a full rollout?
Yes — and we recommend it. A scoped pilot (one use case, bounded data, success metrics agreed up front) de-risks the investment. The discovery call scopes what a credible pilot looks like for you.

Other practice areas

Explore the rest of what we do

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Start with a discovery call.

30 minutes, no obligation. You leave with a clear next step — even if that step isn't us.