Strategy and value
Why AI, where it fits company strategy, and which use cases justify investment.
Applied AI systems · Enterprise AI · Strategy · Implementation
Applied AI under real constraints
Selected work and perspective on AI initiatives where strategy, data, models, infrastructure, governance, and stakeholder alignment all shape the outcome.
AI work is more than model development.
Successful implementation depends on problem framing, measurable value, data readiness, technical feasibility, operating responsibilities, governance, and adoption across the organization.
Data and foundations
Data availability, governance, GDPR, architecture, cloud readiness, and scaling paths.
Implementation and enterprise integration
From prototype to integration, monitoring, validation, and maintainable workflows within existing enterprise landscapes, including SAP Business AI.
Governance and adoption
Roles, risk, compliance, responsible AI, stakeholder communication, and culture.
Selected work
A curated set of applied AI cases focused on technical decisions, operational constraints, and lessons learned.
AI based meter reading
Detection, OCR, and validation workflows for structured extraction from meter images.
Computer visionOCR
Chip detection in manufacturing
Visual anomaly detection designed for production conditions and operator feedback.
EdgeIndustrial AI
RAG chatbot with tuned models
Retrieval, embeddings, graph context, and controlled generation for traceable answers.
RAGGraph data
Edge anomaly detection
Time series anomaly detection deployed on constrained microcontroller hardware.
Time seriesTinyML
Perspective before implementation
AI initiatives require a shared understanding of value, feasibility, risk, responsibilities, and long-term operation before technology choices become meaningful.
Practice beyond projects
Workshops, enablement, mentoring, and professional exchange support the translation of AI interest into concrete use cases, realistic prototypes, and responsible implementation paths.