MCP servers that connect AI to your systems, safely.

The Model Context Protocol is the standard way to give AI assistants and agents access to tools and data. We design and build MCP servers for your internal systems, with the permissions, authentication, and logging a production integration needs.

01why now

The Model Context Protocol, introduced by Anthropic as an open standard in late 2024, defines one way for AI applications to discover and call tools, read resources, and use prompt templates from an external server. Claude supports it across its apps and Claude Code, and support has spread to many other AI clients and developer tools. Build one MCP server for a system and every compatible client can use it.

That changes the integration math. Instead of building a custom plugin for each AI tool your teams use, you build one well designed server per internal system. The hard part is no longer the protocol, which is simple. It is deciding what the AI should be allowed to do, making that safe, and designing tools a model can use correctly.

02what we build

• Servers for internal systems. Your CRM, ERP, ticketing, data warehouse, document store, or in-house application exposed as a set of tools and resources an AI client can use.
• Tool design for models. Tools named, described, and shaped so a model picks the right one and fills the inputs correctly: narrow tools with clear purposes beat one generic "run query" tool every time.
• Authentication and permissions. Users act with their own identity and permissions, not a shared super-user key. Read and write tools are separated, and destructive actions need confirmation or are left out.
• Audit and observability. Every tool call logged with who, what, and when, plus rate limits and alerts on unusual use.
• Remote or local deployment. Hosted servers for teams, or local servers for developer workflows in tools like Claude Code.

design rules we hold to

• Expose the fewest tools that do the job, each with a clear description and example inputs.
• Return compact, structured results a model can reason over, not raw database dumps.
• Treat content coming back from tools as untrusted input, since a document or ticket can contain instructions aimed at the model.
• Test servers with a real model against realistic tasks, not just unit tests on the endpoints.

03who this is for

Companies rolling out Claude or another MCP-capable assistant that want it connected to the systems where work actually happens, software vendors who want their product usable from AI clients, and engineering teams that want their coding agents to reach internal docs, issue trackers, and environments. It pairs naturally with Claude consulting, AI agent development, and AI coding orchestration.

If you only need an off-the-shelf connector for a popular SaaS tool, use the existing one. We build when the system is yours, the existing server is not safe enough, or the tools need to fit your workflows.

04how it runs

1. Map the use cases. Which tasks people want AI to do against the system, which data it needs, and which actions carry risk.
2. Fix the budget. A fixed number for the server, the auth integration, tests, and deployment. Change requests are priced before they start.
3. Build and test with a model. Working server in your hands in two week increments, tested against a set of realistic tasks run through an actual AI client.
4. Security review and rollout. Permissions checked, logs verified, then rolled out to one team before the rest.
5. Maintain. The protocol and clients are evolving, so we stay to keep the server current and extend its tools as usage grows.

Our team builds AI that runs inside real operations, such as the LLM assistant in Supreme Dental's patient apps, wired into the practice's real operations, and brings the same discipline to the integration layer.

05common questions about MCP server development

What is an MCP server?

A server that implements the Model Context Protocol, an open standard introduced by Anthropic, to expose tools, resources, and prompt templates to AI applications. Any MCP-compatible client, such as Claude, can connect to it and use those tools on a user's behalf.

Why build an MCP server instead of a custom integration?

One MCP server works with every compatible AI client, so you build the integration once per system instead of once per AI tool. It also gives you one place to enforce permissions, authentication, and logging for all AI access to that system.

Is it safe to connect AI to our internal systems through MCP?

It can be, with the right design: users act under their own identity and permissions, read and write tools are separated, destructive actions need confirmation or are excluded, tool results are treated as untrusted input, and every call is logged.

Which AI clients can use an MCP server?

Claude's apps and Claude Code support MCP, as do many other AI assistants, IDEs, and agent frameworks. We test against the clients your teams actually use.

How long does it take to build an MCP server?

Scope is fixed up front, so the timeline is set when the budget is. A focused server for one system with a handful of tools is a small project; larger ones ship in two week increments, the most used tools first.

Can you host and maintain the server?

Yes. We deploy remote servers for teams or local servers for developer workflows, and stay past launch to keep them current as the protocol and clients evolve.