Entries by shinchit.han@scieneers.de

PoC vs Prototyp vs MVP vs Pilot

Clarity often only emerges when planning data products or software projects once the development phases have been clearly defined.
Taking a step-by-step approach can gradually reduce uncertainty and align expectations within interdisciplinary teams.

AI Image Generation in Practice

AI image generation has made a massive leap forward since 2025, evolving from distorted results to the production of photorealistic 4K images in real time. In this article, we explore how modern diffusion models work, offer practical tips for structured prompting and demonstrate how to achieve consistency across multiple images using a customer project on storyboard creation. We also explain which models are currently the best and where the technology’s limits lie.

IT-Days 2025

Data science, AI and cloud architectures form the core of our business, but it’s sometimes beneficial to step outside your comfort zone. That’s precisely what our colleague sat scieneers did in mid-December at IT Days 2025 in Frankfurt. We took away ideas from topics such as software architecture, DevOps, agile methods, and digital sovereignty that will directly influence our daily work on scalable RAG systems, clean software architecture, and monitoring.

Throwback to our fall event 2025

Once again, the team took centre stage. All scieneers from Karlsruhe, Cologne and Hamburg came together for a two-day autumn event at the end of September. As well as having some exciting discussions and taking part in some joint activities, we also welcomed five new colleagues. We are now a team of around 50 people!

PyData 2025

PyData Berlin 2025 at the Berlin Congress Center was three days full of talks, tutorials, and tech community spirit. The focus was on open-source tools and agentic AI, as well as addressing the question: How can LLMs be used productively and in a controlled manner? We from scieneers gave a presentation on LiteLLM, titled “One API to Rule Them All? LiteLLM in Production”.

Data science training for real projects

With our ‘Data Science for Everyday Work’ training course, we aim to help companies bridge the gap in practical knowledge when implementing machine learning projects and foster a shared understanding within the team.
We also offer individual modules that can be combined as desired, and we provide consultancy services for your specific data challenges.