Hands-on with your team. By the end, a working connection to one of your systems is running, and you know the rules for the next ones.
No sales pitch. Reply within two business days.
For teams already using AI assistants and wanting to bring them to their own data. And for teams still deciding who want to know what is technically required before they start.
Assistants like Copilot or ChatGPT are in use, but without access to what matters. Data sits on network shares, in an internal Nextcloud, in the CRM, or in a legacy system that runs reliably and was never designed for AI. The question is not whether to connect, but how - without giving up control over who can see what.
Hi, I'm Christian. I take technical ownership for AI and cloud platforms, from build-out through handover to your team. More than twelve years of experience, including several years in technical project leadership and DevOps.
Context: In-house product, in production since 2026.
Starting point: AI agents need access to systems to be useful. A blank SSH or API-token grant is not a sound foundation for that.
Role: Concept, implementation, and operations.
Outcome: A modular MCP server in TypeScript with OIDC authentication, graded permission levels, and a discovery flow so an agent learns available operations per host instead of issuing arbitrary commands.
How MCP works and how it differs from a custom interface. Inventory of your systems. Access and permission model, authentication, logging. Decision on which system to connect.
Implementation against your own system. Test from an assistant. Handover of runbooks and a plan for the next connections.
The standard. Day 1 concept and access model, Day 2 implementation on your system.
Enough if your team already knows which system to connect and credentials are ready on the workshop day.
Maximum six participants so everyone works on their own system on the implementation day. On-site or remote. Travel costs as incurred.
No sales pitch. Reply within two business days.
