Introducing MCP Builds: One Server, Every Major AI Platform

Today we're launching MCP Builds — a fixed-scope service where Queen City AI designs, builds, and deploys a production MCP server that puts your systems inside the AI your team already uses.

If you run a midmarket company, here is the launch in one sentence: we wire your CRM, your database, your internal tools, and your product into Claude, ChatGPT, and Microsoft 365 Copilot — from one build, on the open standard, starting at $20,000, live in three to five weeks.

The Problem We Kept Running Into

Every AI engagement we run eventually hits the same wall. The model is smart. Your team likes it. And it cannot see anything.

Claude doesn't know what's in your CRM. Copilot can't file a ticket in your helpdesk. ChatGPT has never seen your product's API. So your people become the integration layer — copying records into chat windows, pasting answers back out, and quietly deciding the whole thing is more work than it saves.

The old fix was a custom integration per assistant. One for Claude. Another for ChatGPT. A third for Copilot. Each one built differently, secured differently, and broken separately every time a platform shipped a change. For a midmarket team without a platform engineering group, that math never worked.

MCP Changed the Math

MCP — the Model Context Protocol — is the open standard for connecting AI models to tools and data. Anthropic created it, and it is now generally available across all three major assistants: Claude natively, ChatGPT through OpenAI's connectors and Responses API, and Microsoft 365 Copilot through Copilot Studio and the M365 Agents Toolkit.

The practical effect: you build one server to the standard, and it works everywhere the standard is spoken. Your systems become structured tools any of the big assistants can call — read a record, look up a customer, file a ticket, kick off a workflow, pull a report.

Play with the difference yourself:

That's the whole pitch, honestly. The left side of that math is why most midmarket AI integration projects stall. The right side is what we now build as a product.

What an MCP Build Includes

Every build is production-grade from day one — this is a real server your team owns, not a demo that dies after the pilot.

Tool and data design. We map which of your systems, APIs, and workflows belong inside AI, and design a clean, well-scoped tool catalog the model can actually use well. Scoping the tools is most of the craft — a model with fifty vague tools performs worse than one with nine sharp ones.

A production MCP server. Built to the current MCP spec, with structured tools, resources, and prompts, sensible error handling, and rate limits. Hosted, monitored, and tested against real calls from the model — not a mock.

Security as a design input. OAuth or token auth, least-privilege scopes per tool, isolated execution, audited access, and human approval gates on anything irreversible. The model gets exactly what it needs and nothing else. This is the same containment standard we apply to every deployment we run.

Platform connections. Wired into the platform you use today — Claude, ChatGPT, or Copilot — and packaged the way that platform expects. The same server extends to the other platforms without a rebuild.

Docs and handoff. Architecture notes, a tool reference, and a runbook. You own the server when we're done. If you'd rather not operate it, we offer managed hosting — but that's your choice, not a lock-in.

Scope Your Own Build

Most teams start with one server connected to the platform they use most, then extend. Pick what you'd connect and see what the build looks like:

Single-platform builds start at $20,000 and run three to five weeks from kickoff to handoff. Multi-platform extensions add one to two weeks. Managed hosting is a monthly plan, scoped to your setup. We give you a fixed quote after one scoping conversation — no open-ended billable hours.

Why This Matters for Midmarket Specifically

Enterprises solve this problem with headcount. They have platform teams whose job is integration plumbing. You probably don't — and that has meant the highest-leverage version of AI, the one that operates on your real systems instead of next to them, stayed out of reach.

MCP collapses that gap. The integration cost drops far enough that a midmarket operator can put AI inside real workflows — the lead follow-up, the order lookup, the intake classification, the month-end report — without hiring a platform team. We wrote about this shift in Using Claude for Workflow Integrations: the leverage in 2026 is not whether your team uses AI, it's whether AI is integrated into the workflows where the work actually moves.

There's a second angle if you sell software: an MCP server makes your product available to AI agents as first-class tools. As your customers standardize on Claude, ChatGPT, or Copilot, being callable from inside those assistants stops being a novelty and starts being distribution.

How to Start

Book a discovery call with me. Thirty minutes. Bring the two or three systems your team wishes AI could reach, and we'll work out whether an MCP build is the right move, what the tool catalog looks like, and what it costs — a fixed number, before we start.

If you want to go deeper on the offering first, the full breakdown — platforms, packages, security model, and process — is on the MCP Builds solution page.

Frequently Asked Questions

What exactly is Queen City AI launching with MCP Builds?

MCP Builds is a fixed-scope service: Queen City AI designs, builds, secures, and deploys a production MCP (Model Context Protocol) server that connects your systems — CRM, databases, internal apps, product APIs — to Claude, ChatGPT, and Microsoft 365 Copilot. Builds start at $20,000, take three to five weeks, and are handed off with documentation so your team owns and operates the server.

Why is one MCP server better than separate integrations for each AI assistant?

Separate integrations multiply: connecting four systems to three assistants the old way means twelve custom builds, each maintained independently. An MCP server is built once to the open standard, so the same server serves Claude, ChatGPT, and Copilot. Four systems and three assistants becomes one server — one codebase to secure, monitor, and update.

Does this work if my company runs on Microsoft 365?

Yes. MCP is generally available in Microsoft 365 Copilot. We package your server as a Copilot Studio connector or a declarative agent through the M365 Agents Toolkit, so the tools show up inside the Microsoft environment your team already works in. The same server also connects to Claude and ChatGPT if you add them later.

How is a $20,000 MCP build kept secure?

Security is part of the build, not an add-on: OAuth or token authentication, least-privilege scopes per tool, isolated execution, audited access, and human approval gates on any action that cannot be undone. The AI can only do what each tool explicitly allows, and irreversible actions require a person to approve them.

How long until the server is live, and who runs it afterward?

Most single-platform builds go from kickoff to handoff in three to five weeks; adding platforms takes one to two more. After handoff you own the server outright, with a runbook and tool reference so your team can operate and extend it. If you'd rather not run infrastructure, a managed plan covers hosting, monitoring, and updates as your APIs and the MCP spec evolve.

Is my company a fit for an MCP build?

The clearest signals: your team is standardizing on Claude, ChatGPT, or Copilot; you have internal systems or data the model can't reach today; you're tired of one-off integrations breaking; or you sell software and want your product callable by AI agents. A thirty-minute discovery call is enough to tell whether the build makes sense and what it would cost.

Related Reading

• Using Claude for Workflow Integrations: Beyond the Chat Window — The strategic case behind this launch: the leverage in 2026 isn't whether your team uses AI, it's whether AI is integrated into the workflows where the work actually moves.
• AI Agents vs. AI Copilots: Which One Actually Fits the Workflow? — An MCP server is the foundation either one stands on: it's how the model reaches the systems where your work actually lives.
• We Built a Swarm Before We Sold One — How we operate: we run our own AI systems in production before we offer to build yours.

Related Solutions

• MCP Builds — One MCP server. Plugs into Claude, ChatGPT, and Microsoft 365 Copilot.
• Workflow Automation — Connect the tools your team already runs.