Golden Whale and Cevro team up to advance AI-driven player interaction

Golden Whale, a provider of machine learning-driven optimisation and decision intelligence for the iGaming industry, has partnered with Cevro, an AI-powered platform designed to automate and enhance player interactions across support and operational workflows.

At the core of the integration is Golden Whale’s Full Model Control framework, which uses ML models to continuously analyse live player behavioural data, enabling more precise decisioning across engagement and incentives, driven by deeper insight into player value, churn probability and behavioural intent. These decisions can then be actioned through Cevro’s AI agents, allowing operators to guide and control player-facing responses with greater precision across multiple channels, including chat, email and helpdesk, with voice capabilities set to follow.

Cevro’s platform supports a wide range of player-facing processes, including bonus queries, KYC checks, payment-related communications and responsible gaming workflows. By introducing decision intelligence into these touchpoints, operators can move beyond reactive support models and toward more proactive, context-aware engagement, where responses are informed by real-time signals around player value and behaviour.

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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