A standard instead of one-off integrations
MCP connects AI agents to your systems through one interface defined once, instead of building a separate solution for every connection.
Implementing MCP Servers
An AI agent is only as useful as what it's allowed to access. Without a connection to your real data, it gets stuck answering general questions instead of actually taking on tasks. Building a custom integration for every single connection, CRM, ERP, ticketing system, internal database, is expensive and breaks with every update.
The Model Context Protocol (MCP) solves exactly this problem: an open standard through which AI agents access your systems in a controlled way, connected once, usable by any MCP-capable agent. We handle the implementation for you, from strategy through pilot to company-wide rollout.
The topic in brief
Security from the start
Granular access rights, complete logging, and explicit approvals instead of a blank check for the AI agent.
No vendor lock-in
MCP works regardless of which AI model or ecosystem you use, and connects to standard software as readily as to custom-built systems.
Strategy through operation, from one partner
We handle design, piloting, and ongoing operation of your MCP servers.
What is an MCP server?
An MCP server makes a system, a database, an internal API, a CRM, usable by AI agents in a controlled way. It describes which tools and data are available, checks every request against clearly defined permissions, and logs what the agent actually retrieved or changed.
The decisive difference from a classic one-off integration: MCP is an open standard. An MCP server built once works with any MCP-capable AI agent, whether that's Claude, ChatGPT, or a custom agent built into your own product. Switch models later, or add another agent, and the connection still holds.
For you, that means an investment that isn't tied to a single provider, and one that gets more valuable with every additional use case.
Expert tip from InnoGE: "An MCP server is not an end in itself. It only pays off once it's clear what task an AI agent should actually take on for you. That's why we always start with the use case, not the technology."
Tim Geisendörfer
Founder & CEO
Our approach: strategy, pilot, operation
We don't roll out MCP servers across the whole company in one go. We build in three steps that build on each other, so you see early on whether the approach pays off for you, before committing larger resources.
Strategy workshop (1–2 weeks)
Review possible use cases across your business
Assessment by effort and impact, so you start with the highest-leverage case
Resolving open governance and access questions with your IT team
Result: a prioritized list of MCP use cases that make sense for your business
Concept & pilot (4–8 weeks)
Security and role concept for the specific use case
One or two production-grade MCP servers for the prioritized cases
Connected to your real systems and data, not a demo environment
Result: a working pilot, ready for production use
Scaling & operation (from 4 weeks, then ongoing)
Expansion to further teams and use cases
Monitoring, logging, and cost control during ongoing operation
Team enablement, so you can extend further MCP servers yourselves going forward
Result: an MCP setup that grows with your business, instead of stalling at a pilot project
See what our software solutions can do for you
Where MCP servers deliver real value
MCP is deliberately general-purpose, so it applies to very different systems. In practice, we see four recurring use cases in particular:
CRM & sales: look up customer data, prepare quotes, or summarize customer communication, directly from your CRM system.
ERP & operations: look up stock levels, order status, or metrics, without anyone switching between systems manually. More on our page on ERP systems for SMEs.
Internal knowledge bases: search documents, tickets, and support requests and answer them directly, instead of digging manually through multiple systems.
Custom business software: expose your own APIs and databases to AI agents in a controlled way, instead of leaving them out entirely.
Security of MCP servers
Giving an AI agent access to your systems sounds like a risk at first. Done right, an MCP server is actually more controlled than many existing integrations, precisely because every access is governed and logged individually.
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Explicit consent
Critical actions require approval, instead of executing automatically.
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Least-privilege access
The agent only sees the data and functions approved for its specific use case.
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Complete logging
Every query and every action is traceable after the fact.
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Separation by team, project, or tenant
Different parts of your business get their own, separate access.
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Integration, not replacement
The MCP server fits into your existing access management, instead of creating a new, parallel structure.
Expert tip from InnoGE: "If you're giving an AI agent access, you should know exactly what to, and why. That's why we always start with the narrowest permissions that still work for the use case, and expand only when needed."
Tim Geisendörfer
Founder & CEO
Ready to connect AI agents to your systems, securely?
From the first strategy workshop to production operation, we'll support the implementation of your MCP server.
FAQ – Implementing MCP servers
An MCP server is a standardized interface that lets AI agents access a system, database, or API in a controlled way. It defines which tools and data are available, checks every request against set permissions, and logs what was actually retrieved or changed.
No. MCP is an open standard and works regardless of the AI model or provider in use. An MCP server built once can be used with any MCP-capable agent, whether you're working with Claude, ChatGPT, or a custom-built agent today.
Security is part of the concept phase from the start: granular access rights, explicit approval for critical actions, complete logging, and integration into your existing access management. The agent never gets more access than the specific use case requires.
Usually not. An MCP server connects to your existing systems, whether that's standard software like a CRM or ERP, or custom-built applications, without those systems needing to change themselves.
That depends on the scope and number of use cases. That's why we deliberately start with a compact strategy workshop, where we work out together what scope makes sense for you before committing larger resources.
A first pilot covering one or two use cases is typically ready within four to eight weeks. We plan the subsequent expansion to further teams and use cases together with you, depending on how fast you want to scale.