How Betano turned Customer Support into a Strategic Asset
Moveo AI Team
in
📖 Case Studies
In iGaming, scale no longer wins.
Any iGaming operation can automate conversations, but very few can make every conversation smarter than the last.
Betano, operated by Kaizen Gaming, went looking for exactly that.
Across 19 markets, 10 product categories, and millions of player conversations, the partnership with Moveo.AI built an intelligence layer that learns from every interaction and compounds over time.
The result is a retention rate above internal targets and a CSAT close to human-assisted support, something rare at this scale.
This article traces how the decision was made, how the implementation was conducted, and what the results reveal about the future of customer support in iGaming.
The challenge of scaling without losing sight of the player
The iGaming sector operates under a regulatory framework that is still consolidating across many markets.
Players don't want to be served, they want to be recognized. They want the system to know who they are, what they've done before, and what they need right now, without having to repeat themselves.
Across a platform operating in multiple markets, with players of completely different profiles and interests spanning live sports betting to casino products, that is exponentially harder to deliver.
For Felipe, Operations Manager at Betano Brazil, the operational diagnosis was clear. The challenge was not just about volume, it was about diversity.
Betano serves customers across very different socioeconomic profiles, with varied consumption habits and interests spanning dozens of products on the platform.
A system without memory cannot serve that base with consistency. And an agent that does not remember the previous conversation cannot build any kind of trust.
"Within operations, our main challenge is understanding which automations deliver value to the customer at different stages of the journey, so we can create scalability for the business." - Felipe, Operations Manager, Betano Brazil
Scaling without losing sight of the player requires a model that learns, not just faster response times. The solution lies in an architecture capable of deepening its understanding with every new interaction.
Why Betano chose intelligence, not automation
Before deciding on Moveo.AI, Betano's primary concern was reliability. On a platform where customers expect immediate resolutions, an agent that misinterprets a request or loses the thread of a conversation mid-interaction is not just a technical failure. It is a direct retention risk.
The system needed to combine two characteristics that rarely appear together in AI solutions.
The first was versatility: the ability to serve very different customer profiles, with distinct contexts and intentions, without losing coherence throughout the conversation.
The second was reliability: consistent responses, even in complex flows, without the kind of drift that erodes user trust.
Moveo.AI was chosen precisely for delivering that combination.
Unlike script-based solutions, memory-enabled agents analyze every conversation and use that learning to calibrate the next response. Over time, the system knows more about the player than any rule could anticipate. It is that compounding intelligence, not response speed, that explains the results.
"Shortly after the start of our collaboration with Moveo, the AI agents were already interacting accurately and reliably with our customers, earning and maintaining that trust every day." - Felipe, Operations Manager, Betano Brazil
How the implementation was conducted
One aspect that stands out in Betano's experience is the nature of the implementation process. It was not a standard technical delivery where the platform is installed, and the client configures flows independently. It was a co-building process, with the Moveo team working alongside Betano's operations to develop and adapt each agent to the specifics of the business and its customers.
That makes a practical difference: the workflows were designed based on what actually happens in real conversations, not on assumptions about user behavior.
With an average go-live of under one month per market and more than 10 new markets launched in a single year, the implementation was built to work at real scale, not in controlled conditions.
An intelligence that knows when to resolve and when to connect
One of the most important decisions in the implementation was defining how agents should behave depending on the stage of the customer journey. Felipe describes two distinct roles that emerged from that process:
When the customer needs something practical and objective, the agent ensures that the request is understood precisely. No ambiguity, no unnecessary back-and-forth.
When the context calls for engagement, the agent keeps the customer in the conversation with a warmer interaction style, adapting tone and pace to the user's profile.
Most AI solutions treat all interactions the same way. The result is predictable: cold when the player needs warmth, verbose when they need directness.
With memory, the agent carries the context of the interaction and determines which role to play at any given moment, without the customer noticing the transition.
Is your operation ready for AI agents with memory? Take the Readiness Assessment →
What happens when every conversation feeds the next one
The result that stands out most in Betano's account is not operational efficiency in itself. It is what explains that efficiency: a system that analyzes every conversation and uses that learning to calibrate the next one. That is what makes a CSAT close to human-assisted support possible at this scale.
This happens because Moveo.AI agents do not follow fixed scripts. They operate based on the accumulated context of the interaction, which enables more calibrated responses and more natural conversations.
The customer does not feel like they are navigating a menu. They feel like they are being helped by someone who understands what they need.
"We achieved good retention, above our target, and satisfaction very close to human levels, which is rarely seen in a 100% automated flow. Support became more flexible and is able to be closer to the user." - Felipe, Operations Manager, Betano Brazil
The retention result also deserves separate attention. In iGaming, user retention is directly tied to the quality of the support experience.
An agent that frustrates the user accelerates churn. An agent that resolves accurately, adapts to the moment, and remembers the customer's history encourages the user to stay on the platform.
AI as part of the backbone of an operations strategy
What Betano has built with Moveo.AI is not a pilot project or a parallel support solution. It is a core structure of the operations strategy.
AI agents operate across different support types and different levels of adoption, depending on the market and the stage of the customer journey, but the principle is the same across all contexts.
"AI agents are part of the backbone of our strategy, with different types of support and different levels of involvement. What matters is knowing that when a customer needs something very practical and direct, the AI's role is to ensure their request is understood exactly. In other situations, the AI plays a more relational role, ensuring engagement and involvement throughout the conversation." - Felipe, Operations Manager, Betano Brazil
This reflects an operational maturity that translates into concrete results: AI is not treated as a point-in-time cost reduction tool, but as a customer relationship infrastructure.
Across 19 markets and 10 product categories, the system grows with the business and deepens its understanding of every player with every interaction.
What Betano is building that others cannot replicate
The difference between what Betano has built and what most iGaming operators have today is not technological, it is structural.
Betano built a system that analyzes every conversation, accumulates what it learns across all markets and categories, and makes every interaction smarter than the last.
That cannot be replicated with scripts, menus, or fixed rules. It is the result of an intelligence layer that grows with the business and with every player.
Want to understand how to structure memory-enabled AI agents in your operation? Book a demo →