Golden Whale, a specialist in machine learning-powered optimisation and decision intelligence for the iGaming sector, has announced a new partnership with Cevro, an AI-driven platform focused on automating and improving player interactions across support and operational processes.
The collaboration centres on Golden Whale’s Full Model Control framework, which leverages machine learning models to continuously evaluate live player behavioural data. This enables operators to make more informed decisions around engagement strategies and incentives, based on insights into player value, churn likelihood and behavioural patterns. These insights can then be executed through Cevro’s AI agents, giving operators greater control over player-facing communications across channels such as chat, email and helpdesk, with voice functionality expected to be introduced at a later stage.
Cevro’s technology facilitates a broad spectrum of player-related interactions, including handling bonus inquiries, conducting KYC procedures, managing payment communications and supporting responsible gaming initiatives. By embedding decision intelligence into these touchpoints, operators can transition from reactive customer support to a more proactive and context-driven approach, where responses are guided by real-time data on player activity and value.
Eberhard Dürrschmid, CEO at Golden Whale, said:
“Operators are managing an increasing number of player communications across different touchpoints, and many of those processes are still handled in a reactive or manual way. By working with Cevro’s AI agents, we can connect our decision intelligence directly into these communication workflows, enabling operators to respond more precisely and consistently, while also reducing the complexity of managing these interactions at scale.”
Chaim Heber, CEO at Cevro, added:
“Our focus is on helping operators automate and scale player interactions without losing quality or control. By integrating Golden Whale’s decision intelligence, we can bring a new level of precision into how our AI agents operate, using deeper signals around player value, churn risk and behaviour to guide responses. This not only improves resolution and efficiency but also allows operators to deliver more meaningful interactions that support retention and long-term player value.”
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