Why operator control matters for AI
Operators need control over where their data is held and how it is used as AI becomes a larger part of their business. Our platform architecture is designed to support that control, giving partners dedicated environments in which to develop and use AI capabilities.
Speaking to the iGaming Round Table, Pablo Jensen, our Chief Technology Officer, explained how this approach connects to commercial performance, player protection and the way we build technology.
“I think it’s about controlling the data and data sovereignty,” he said.
Keeping AI close to operator data
Our single-tenant architecture gives each client its own environment. Depending on the configuration, that can be hosted in the cloud, on the operator’s premises or in our data centres. Shared environments are also an option, with the setup reflecting the operator’s requirements.
For partners that want their data ring-fenced within their own data centre, AI models can run and learn within that environment. The choice of hosting therefore matters to how operators put AI to work and maintain control over the information it uses.
Pablo also highlighted our investment in local language models, with the aim of keeping model training within the client environment. This extends the same principle to new AI capabilities as they are developed.
Connecting technology to commercial results
We already use AI across our casino and sportsbook products. Bonus Guardian, for example, helps operators identify potential bonus abuse and determine where action may be needed.
Our proprietary Bet Builder provides another example. Pablo explained how AI supports recommendations within the product, helping present players with relevant betting options.
Discussing its performance during the World Cup, he cited a revenue increase of around 18% compared with the previous third-party Bet Builder implementation. For Pablo, that provides a concrete example of the commercial impact of bringing the technology in-house.
These applications give operators specific uses for AI within their existing business, backed by the data available through the platform.
Combining platform capabilities with specialist expertise
Player protection is another area where the platform can bring together different capabilities. Pablo highlighted our work integrating Mindway AI’s GameScanner, which helps identify behavioural patterns associated with players at risk.
That work draws on specialist expertise, including psychologists, to understand what patterns in player data may indicate. It complements the configurable limits operators can apply according to their requirements and the regulations in their market.
The aim is to support player protection through both those limits and additional behavioural indicators that help operators identify where attention may be needed.
Changing how development teams work
Alongside AI in our products, we are exploring how it changes our own engineering and business functions.
Pablo described selected teams working on new projects with AI from the outset. He expects team structures to evolve, with more emphasis on product management and specialists who can define the requirements that guide AI-assisted development.
His expectation is that this will lead to more teams, each with fewer people. Repetitive business tasks also offer scope for automation, while staff can develop skills in defining requirements and directing the work.
For operators, Pablo’s wider point is to consider what the full platform makes possible. A partnership can begin with one part of our offering and expand over time, drawing on the broader data platform as the operator’s needs develop. Retaining control of that data remains central to the approach.
