AI for Mechanical Engineering: Consulting & Development

Typical applications are source-based assistants for standards, specifications and project knowledge. Added to that are the automated creation of technical documentation, knowledge management that keeps experience available despite skills shortages, and the analysis of large volumes of technical documents and data. The best entry point is usually a clearly scoped, recurring bottleneck.

Mechanical engineering and industry are document- and knowledge-intensive: standards, drawings, bills of materials, project history. This is exactly where AI excels, because it makes unstructured knowledge accessible and usable. At the same time it preserves experience that would otherwise leave with senior staff.

The intro call is free. A clearly scoped proof of concept or MVP is usually feasible within a few weeks. Larger integrated solutions run over several months. We deliberately start small and measurable so the value shows early, before scaling.

Technical data is often the most valuable know-how. We work GDPR- and AI-Act-compliant, with EU hosting as standard and on-premise on request. With RAG your knowledge stays in your controlled database, and access is managed via role-based permissions. Our product KoAssist demonstrates this approach for design teams in practice.

First a needs analysis and use-case selection, then a prototype on a scoped area (for example one set of standards or a project archive), testing with real design engineers, followed by integration and operation. This shows results early and keeps effort and risk low.