Make Your Internal Systems AI-Ready, Once
Build Model Context Protocol servers that expose your CRM, CMDB, ERP, and databases as secure, permissioned tools any AI client can use without rebuilding the integration for every assistant or agent that follows.
System 06 - what we ship
What Are MCP Servers?
Model Context Protocol is an open standard that defines how AI models connect to external tools and data sources. An MCP server wraps your internal systems and exposes them as permissioned tools that any compatible AI client, Claude, ChatGPT, or your own agents, can call. Build the integration once. Every AI assistant and agent that follows can use it without a new integration project for each.
- Best for
- Enterprise Claude and ChatGPT rollouts
- Best for
- Agent tool layers
- Best for
- Platform AI-enablement
IT and platform teams evaluating enterprise AI rollouts
Where connecting AI to internal systems is the bottleneck preventing adoption at scale and each integration project currently happens separately, with no shared foundation.
Organisations deploying Claude for Work or ChatGPT Enterprise
And needing to connect those AI clients to internal data, such as CRM records, configuration databases, product catalogs, and employee directories, in a controlled, permission-bounded way.
Engineering teams building agent fleets
That need reliable, permissioned, auditable access to business systems as tools without building bespoke API integrations for each agent.
Companies with valuable internal platforms that are not yet AI-accessible
Where the knowledge and data that would make AI useful are locked inside systems that external AI clients cannot reach.
- 01
Platform Inventory
We map the systems that hold the context AI needs, such as customer data, configuration records, knowledge repositories, and transactional history, and the permission model each system requires for AI access.
- 02
MCP Server Design
Tool definitions, schema design, and response formatting for each system. Well-designed tools surface the right context for the AI client without exposing what it does not need and without creating surface area for unintended data access.
- 03
Authentication and Permissions
Role-based access controls, audit logging, and the permission boundaries that keep AI tool use within approved scope. The same governance standards as any other API your systems expose.
- 04
Build and Testing
Server implementation, end-to-end testing with the AI clients that will use it, and validation for the query patterns the model will attempt but should not succeed with.
- 05
Documentation and Expansion
Tool documentation in a format AI models can consume. Usage analytics. An expansion roadmap for additional systems when the first MCP server is in production.
- CRM access for sales and customer support AI assistants
- CMDB and infrastructure data for NOC agents
- Product and inventory databases for customer-facing assistants
- HR systems for employee self-service AI
- Compliance and audit logs for monitoring agents
- ERP data for operational AI workflows
Platform engineering background
We have built the CRMs, CMDBs, and multi-tenant enterprise platforms that MCP servers expose. We understand the data model, the permission logic, and the edge cases from the inside, not from API documentation.
Security-first tool design
MCP tools that expose internal systems require careful scope definition. We design the access boundaries before the endpoint because the risk in an overpermissioned tool is not theoretical.
End-to-end delivery
MCP server build, AI client integration, permission model, and end-to-end testing in a single engagement. There is no separate team for the platform side and the AI side.
Cross-client compatibility
We test against Claude, ChatGPT, and custom agent configurations. The same MCP server works across all compatible AI clients without modification for each.
Next step
Build an AI Strategy Your Team Can Act On
Start with a platform inventory. We map your internal systems, design the MCP server architecture, and deliver a roadmap for making your platforms AI-ready.