About Dennis
- Built an AI Lab from scratch and led it as Founder & CTO
- Brought Enterprise AI copilots and retrieval systems into production using real company data
- Commissioned by enterprise and regulated organizations for architecture, risk, and governance
- Recognized by HPE for technical innovation in the enterprise AI space
- Open-source contributions in Model Serving, Triton kernels, and retrieval systems
- ACM publication on LLM training and system performance on AMD MI300A infrastructure
German
Native or bilingual
English
Fluent
Experience
- SeedboxManaging Director / Partner, formerly CTODIGITAL AND ITJanuary 2020 - March 2026 (6 years and 2 months)Stuttgart, GermanyI help companies implement AI reliably on real enterprise data. From feasibility to architecture to operationalization.My focus is on Enterprise AI, Knowledge Systems and Architectures, Retrieval/RAG, Document Intelligence, and the data layer behind productive AI systems. I work where AI initiatives often fail in practice: fragmented knowledge sources, weak retrieval quality, unclear target architecture, high costs, fragile PoCs, and governance not translated into technical reality.I combine strategic positioning with technical depth and particularly enjoy working on questions such as:- AI Readiness Reviews and technical due diligence- Retrieval/RAG architecture and quality analysis- Knowledge architectures for internal company data- Document Intelligence, structured extraction, and data preparation- Build-vs-buy decisions for AI stacks and knowledge platforms- Translation of AI Governance / EU AI Act into technical controls and operating models- Target state, prioritization, and roadmap for productive AI systemsTypical challenges I help with:- unstructured or distributed company data- poor answers despite existing models- fragile AI/agent workflows- high inference or processing costs- lack of reliability, traceability, and ownership- PoCs without a clear path to productionMy profile is particularly suitable for companies that don't just want to "build something with AI," but need clarity on what works with real data, which architecture is viable, and how to turn it into a robust system.Technical focus areas:Enterprise AI, Retrieval, RAG, Knowledge Systems, Document Intelligence, structured extraction, Entity Linking, Multimodal Retrieval, Data Pipelines, AI Architecture, AI Governance, Vector Search, Late Interaction, Operationalizing AI systems.
- 90degrees.ioBuilder | Engineer | ResearcherDIGITAL AND ITApril 2026 - Today (4 months)Consulting and development around Enterprise AI on real company knowledge: Proof of Concept, architecture, retrieval, and governance.- AI Readiness Reviews and technical due diligence- Retrieval/RAG architecture and quality analysis- Knowledge architectures for internal company data- Document Intelligence, structured extraction, and data preparation- Build-vs-buy decisions for AI stacks and knowledge platforms- Translation of AI Governance / EU AI Act into technical controls and operating models- Target state, prioritization, and roadmap for productive AI systems
- 1886VenturesHead of Product / Lead Product ManagerAUTOMOBILEJanuary 2017 - January 2020 (3 years)Stuttgart, GermanyProduct strategy, venture building, and development of new business areas.Leading and managing product teams in Germany, heading the Stuttgart location.Development of complex digital products including validation, rollout, and stakeholder management within the corporate group.
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