Artificial intelligence is increasingly moving beyond experimentation and into real business deployments. That’s why we launched AI in Production — a new knowledge-sharing channel where we discuss hands-on experience from projects delivered for our clients.
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Why Did We Launch AI in Production?
AI in Production was created for those who are no longer asking whether to implement AI, but how to do it effectively and responsibly.
We talk about how AI operates in real organizations — within systems that must run 24/7, in architectures built over years, and in processes that directly impact operations and financial results.
In upcoming episodes, we cover topics such as:
- real-world AI implementations in companies,
- business process automation,
- solution architecture (including Azure, APIs, RAG),
- data quality challenges,
- limitations of AI models and how to control them,
- cost optimization and scaling strategies.
We share lessons from client projects — what worked, what didn’t, and what we learned along the way. We present both the technological and business perspectives.
What Is the First Episode About?
In the premiere episode of AI in Production, we talk with Mariusz Mrowiec, Head of Business Development at VM.PL, about the practical implementation of an AI-powered business chatbot — from concept and technical architecture to insights after deployment in a production environment.
We discuss:
- the 80/20 Pareto principle in AI projects,
- why data structure and data quality became the biggest challenge,
- the technical architecture of the solution (Azure, API, RAG),
- when and why to use different GPT models,
- how to limit chatbot responses to align with business objectives.
One of the key takeaways is that technology itself is rarely the biggest obstacle in AI projects. More often, the real issue lies in data — inconsistencies, gaps, and fragmentation across the organization.
Why Listen to AI in Production?
If you’re planning to implement AI in your organization — or simply want to understand how real-world deployments actually work — this podcast offers a practical perspective on AI projects in production environments and beyond.
It’s a space for anyone who wants to make informed AI decisions based on real project experience rather than marketing promises.
During the project, we created an internal summary of key insights and recommendations. Based on this, we prepared an article outlining the most important lessons learned from implementing AI in a production environment.
Interested? Get in touch with us. We’d be happy to discuss your challenges, explore AI opportunities within your organization, and share insights gained from hands-on project delivery.
