Model Context Protocol (MCP)

We work with you from strategic assessment to a production-ready system – from identifying the right use cases (for example in our AI Opportunity Mapping Workshop) through the architecture and development of AI systems to integration into your existing system landscape.

AI agents are systems that independently handle complex tasks, make decisions, and interact with your existing systems. They are particularly suited to processes that rule-based automation cannot handle – for example due to too many edge cases or media breaks. We support the integration of AI agents into your workflows, using open standards such as the Model Context Protocol (MCP).

In practice, AI initiatives rarely fail because of the technology itself, but rather because of unresolved integration, security, or architecture questions. This is precisely where INNOQ comes in: we ensure your AI solution doesn’t remain in the lab but works reliably in production.

Data Mesh is an approach to decentralized data architectures with clear domain ownership. Instead of a central data platform that becomes a bottleneck, teams take ownership of their own data – through data products, data contracts, and self-service platforms. This creates the data quality and accessibility without which AI projects cannot scale.

Yes. Not every organization is able or permitted to use external AI providers' models – whether for regulatory reasons, data privacy requirements, or strategic considerations. We advise on running open-source models on your own infrastructure or in a private cloud, and provide TCO analyses covering hardware and operating models.

Our goal is enablement, not dependence. We rely on transparent, modular architectures rather than black box solutions – model-agnostic and free of vendor lock-in. We also train your teams through hands-on coaching programs such as the Agentic Engineering Accelerator.