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.
One workflow to production. One platform to build on what works.
Positioning
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
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
Three server types, one protocol. We build the right combination for your use case.
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.
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.
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.
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
From discovery to production in 4 weeks. Boring excellence, not innovation theater.
Step 01
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 02
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 03
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 04
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
The most practical questions that usually come up before a first application actually lands in the operation.
View all questionsAn MCP (Model Context Protocol) server is a backend service that exposes your enterprise data and tools to AI assistants like Claude in a standardized, secure way. Without one, your AI tools are limited to general knowledge. With a custom MCP server, they can access your actual business data, run queries, and execute workflows.
We primarily use TypeScript and Python - the two officially supported MCP SDKs from Anthropic. TypeScript for high-performance, event-driven servers. Python for workloads that integrate with ML pipelines, data science tools, or scientific computing. We pick the right tool for each server based on your ecosystem.
Security is built into every layer. We implement OAuth 2.0 for authentication, role-based access control (RBAC) for authorization, and comprehensive audit logging for compliance. Data stays within your infrastructure. Every MCP tool call is validated, rate-limited, and logged.
Yes. We've built integrations with SAP, Salesforce, Microsoft Dynamics, HubSpot, ServiceNow, Jira, SharePoint, and many custom systems. If your system has an API, we can build an MCP server for it.
First step
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
