We Build the MCP Servers That Power Your
AI
Your AI assistant is only as useful as the data it can reach. We build custom Model Context Protocol servers that give Claude, Copilot, and other AI tools secure, real-time access to your enterprise systems - ERP, CRM, databases, SharePoint, and more.
Positioning
Why You Need Custom MCP Servers
Model Context Protocol is the open standard that lets AI assistants talk to your systems. But the protocol is just a specification - someone needs to build the servers.
That's us. We engineer custom MCP servers that expose your enterprise data, tools, and workflows to AI in a controlled, secure, and scalable way.
No generic connectors. No brittle workarounds.
Purpose-built servers designed for your specific systems, your security requirements, and your scale.
Outcomes
What You Get
AI assistants that query your databases, ERP, and CRM in real time
Secure data exposure with fine-grained access control per user and role
MCP resource servers, tool servers, and prompt servers tailored to your domain
Full audit trail of every AI interaction with your enterprise data
Servers that run on your infrastructure - cloud, on-premises, or hybrid
Compatibility with Claude, GitHub Copilot, and any MCP-compliant client
MCP Server Types We Build
Three server types, one protocol. We build the right combination for your use case.
01
Resource Servers
Expose your enterprise data as structured resources that AI can read and understand. Connect knowledge bases, document repositories, SharePoint libraries, and databases so AI assistants can answer questions with your actual data.
02
Tool Servers
Give AI the ability to take action in your systems. Build MCP tool servers that let assistants create tickets, update records, trigger workflows, run queries, and execute business processes - all with proper authorization and validation.
03
Prompt Servers
Codify your organization's domain expertise into reusable prompt templates served via MCP. Ensure consistent, high-quality AI outputs aligned with your business rules, compliance requirements, and industry terminology.
04
Enterprise Integration Layer
Most organizations need a combination of all three server types, connected to multiple backend systems. We architect and build the complete MCP integration layer - with authentication, rate limiting, monitoring, and failover built in.
Approach
How We Build Your MCP Server
From discovery to production in 4 weeks. Boring excellence, not innovation theater.
Step 1
System Mapping
We map your enterprise systems, data flows, and access patterns. Which systems should AI reach? What data is sensitive? What actions should AI be allowed to take? We define the scope and security boundaries before writing a single line of code.
Step 2
Server Architecture
We design the MCP server architecture: resource schemas, tool definitions, authentication flows, and deployment topology. TypeScript for performance-critical servers, Python for ML-adjacent workloads. Always production-grade from day one.
Step 3
Build & Integrate
We build the server, connect it to your backend systems, implement OAuth 2.0 and RBAC, and add comprehensive audit logging. Every server is tested against real data with real access patterns.
Step 4
Deploy & Harden
We deploy on your infrastructure using Kubernetes and Terraform. Rate limiting, error handling, monitoring dashboards, and alerting - all configured. You get the source code, the documentation, and a team that knows how to maintain it.
FAQ
Frequently Asked Questions
The most practical questions that usually come up before a first application actually lands in the operation.
First serious step
Ready to Build Your MCP Server?
Tell us which systems your AI needs to reach. We'll map the architecture and show you what's possible - in a free 90-minute roadmap session.
Included in the first conversation
Ready to Build Your MCP Server?
Tell us which systems your AI needs to reach. We'll map the architecture and show you what's possible.
Response time
We typically respond within 24 hours