On September 21, vendor Gamblitude announced it had signed Polish sports brand BETFAN. This deal was not about buying a single module, but about signing an entire five-piece suite: data warehouse, self-service reporting, automated monitoring, conversational AI assistant, and player behavior prediction models. BETFAN vice chairman Jakub Swisowski put it directly: they do not care about buzzwords like technology and AI, only about the actual impact on the business—faster decisions, faster product iteration, and a better player experience.
The timing is interesting. Gamblitude's AI assistant was only released publicly on September 17, and four days later it appeared in BETFAN's contract list. With the product launch and customer signing compressed into the same week, either the contract had already been negotiated and the announcement was held until the launch, or the deal closed immediately after release. The official statement did not say which, nor did it disclose the contract term or amount. One thing is clear: BETFAN's team wants to be able to query and ask questions on its own once it has the data, without having to queue up behind the technical department.
The vice chairman also said something worth noting: the goal is to become the first "second choice" in the Polish market. There is no claim of overtaking anyone; it recognizes that the leader cannot be replaced in the short term, so it aims to secure second place first. This positioning is more practical than most slogans, and it also shows that BETFAN's procurement this round was calculated: hand the data layer to professionals and focus its own energy on product and customer acquisition.
This news gives teams currently evaluating platform solutions one more reference point. In the past, everyone assumed the data layer had to be built in-house, and it was normal for reporting to involve waiting in line for developers and for metric definitions to be inconsistent. BETFAN's approach is to hand the entire data pipeline after gaming API integration to one company, with the warehouse, dashboards, alerts, and models all configured at once. When comparing against the evaluation checklist for an integrated platform solution, one more item can be added: whether the data layer is built in-house or follows the platform, and how data migration is calculated during a switch—put it in the contract before discussing further.
Teams that are unsure can contact us and calculate both paths for the data layer—in-house build and outsourcing—side by side.