It's #buildWithAI Time!

We are happy to have two speakers:

Corvin Schapöhler (Novatec Consulting):

Corvin is Senior Consultant at Novatec Consulting - now part of CGI. With a special focus on Cloud, Corvin enables teams in developing Cloud native applications, supports with CI/CD setups and pushes for team-setups that enable DevOps wherever possible. Recently he picked up an interesst in the adoption of LLMs and AI in general, to implement real world use cases at different customers.

In his freetime, he runs a small homelab to reduce his dependency to US based technologies and play around with all the cool tech, that is open to use

Abstract: Show and Tell - Local LLMs, AI Chats and more

AI and the cloud go hand in hand. And the operation of models is extremely resource-intensive. But what if I don't have access to a cloud? My Azure credits for this year are already used up? Or I have data that I don't want to store in the cloud? Can I then not use this technology at all?

I would like to show you my experience with local alternatives. These even run on mini PCs or laptops and can offer enormous added value.

Meenu Khurana (Adevinta):

Meenu is a software engineer with over 10 years of experience building scalable systems using Java, Spring Boot, SQL, and NoSQL. Passionate about software architecture and clean code, she is now embracing AI to shape the future of development. Meenu combines strong engineering principles with a curiosity for innovation.

Abstract : Bringing AI into the Heart of Java Microservices: A Journey to Vector Search and LLM Integration

In this session, I will share how we integrated vector embeddings and large language models (LLMs) into a Java Spring Boot microservice to enable fast and accurate similarity search. I will walk through the practical evolution from traditional database-driven solutions to AI-enhanced architectures, explaining how we adapted our system design, data flow, and infrastructure to support machine learning techniques. And how we gained hands-on insights into blending classical microservice patterns with AI innovation — and lessons learned from applying these ideas in real-world production environments.