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All of the above. It's really challenging to extract data from systems and place it correctly. For example, in the education sector where I primarily worked, we had about 25 consultants dedicated to data migration. Additionally, our DevOps teams, consisting of 10 people, focused on transferring data from other solutions into our database. It's quite a complex task. When you have around a thousand customers per company that we aim to serve with our solutions, the cost can exceed €100,000. However, companies are often unwilling to pay that price, so we typically offer it at a reduced cost of around €50,000 or €40,000. This depends on the financial strength of the company we are assisting. Our focus is on the long-term game and creating cash flow, which sets us apart from most competitors. They lack the financial flexibility to reduce implementation costs and don't have the necessary experience. If you examine the sectors where Topicus is active, you'll see that Topicus is still gaining market share, while other businesses are losing theirs and failing to win tenders. At Topicus, competitors struggle to match us in solutions, consultancy, and pricing.
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Initially, Topicus underestimated the impact of AI. Two years ago, during a summit in Toronto with various CEOs active at Constellation Software, there were different workshops. Only one out of 500 workshops was about AI. At that time, AI wasn't considered a big deal; it was more about reading, making films, or enhancing text. They didn't consider AI as a tool for building more code or creating insights from the vast information in our systems.
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Topicus has the potential to gain significantly if they can create locally managed LLMs and use them on their data, whether in healthcare, government, or education solutions. When I was at Topicus, we did a pilot with AI-based fiber coding and developed a search engine within one of our propositions in just two weeks. It was something we wanted for over three years but couldn't achieve due to cost inefficiencies. With AI, and some experimentation we accomplished it in a few weeks. It's now one of the most used features in the solution we had.
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