The challenge
During high-volume periods, such as major sporting events, high-profile tournaments, or promotional campaigns, Crystalbet experiences sharp surges in customer inquiries. These typically include questions related to deposits and withdrawals, account access, verification, bonus terms, and bet settlement. Without automation or scalable tooling, support teams face increased operational pressure: queues form quickly, response times increase, and maintaining consistent service quality becomes more challenging during peak demand.
The solution: AI agent implementation
To address these challenges, Crystalbet adopted an AI agent (chatbot / virtual assistant) integrated directly into its customer service channels. The AI agent automatically handles routine and frequent customer queries, while seamlessly escalating more complex or sensitive issues to human operators.
Features of the system include:
- A structured knowledge base covering FAQs, account management, payments, bonuses, and game rules
- Smart fallback escalation when the AI cannot confidently resolve a request
- Multichannel access across web and mobile platforms
- Monitoring of key indicators such as automated resolution coverage, first response behaviour, and escalated tickets
With Briano's support, we are able to handle peak traffic far more efficiently. Customers get answers right away, while our teams can focus on complex cases that truly require human attention.
The results
Crystalbet’s published case study reports that, in its most recent observed month, the AI agent independently resolved approximately 45-50% of incoming chat requests end to end. These included routine questions about payments, account access, bonuses, verification, and site navigation.
Crystalbet receives tens of thousands of chat requests per month. Demand can rise several times above the usual level during major sporting events and promotional campaigns.
Smart escalation efficiency
The AI provides the first response in 100% of conversations. Requests that need a human are handed over with a structured summary of the customer’s latest question, giving operators context for the next step.
Operator observations indicated fewer queues and less waiting pressure during peak periods. Detailed waiting-time and customer-satisfaction dashboards were still being finalized in the published story, so these improvements should be treated as qualitative observations.
Conclusion
The combined workflow gives customers an immediate first response, automates routine questions, and preserves human handling for complex or sensitive cases. The published story describes a more scalable support operation, with further measurement needed to quantify the full impact.
