How Betano turned Customer Support into a Strategic Asset
Moveo AI Team
in
📖 Estudos de Caso
Scaling customer support in the iGaming sector is inherently complex.
A platform needs to serve a diverse user base, with very different profiles, across products ranging from live sports betting to casino games, and do it accurately, around the clock, without compromising the experience.
Betano, operated by Kaizen Gaming, tackled this challenge directly through a partnership with Moveo.AI. The results, since the start of the collaboration, include a retention rate above internal targets and a CSAT score close to human-assisted support (something rarely achieved in fully automated operations).
This article traces how the decision was made, how the implementation was structured, and what the results reveal about the future of customer support in iGaming.
The challenge of serving a diverse base without losing consistency
The iGaming sector operates under a regulatory framework that is still consolidating across many markets. This creates a specific combination of pressures for operations teams: scaling quickly, maintaining regulatory compliance, and delivering a support experience that customers perceive as reliable and personalized.
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 fixed support script does not serve that base with consistency, and an automated flow without context fails precisely when the customer needs an accurate response the most.
"Within operations, the primary challenges lie in identifying which automations deliver customer value across different stages of the journey, thereby providing business scalability." - Felipe, Operations Manager, Betano Brazil
Scaling while maintaining quality requires a system that knows when to be direct and when to be relational. That distinction is what separates a memory-enabled AI agent from a conventional automation tool built on menus and predefined responses.
Why reliability was the decisive criterion
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 automation platforms.
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 accumulate context throughout the interaction. This allows responses to be better calibrated to each moment, without customers needing to repeat information or notice that they are interacting with an automated system.
"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 structured
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.
The two roles of the agent across the journey
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 automation platforms treat all interactions the same way. The result is that they are either too cold in situations that call for warmth, or too lengthy in situations that call for 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 a CSAT close to human support actually means in an automated operation
The result that stands out most in Betano's account is not operational efficiency alone. It is the CSAT score. Achieving customer satisfaction that rivals human-assisted support in a fully automated flow is rare enough to deserve analysis.
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 automation layer. 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.
What Betano's experience reveals about iGaming customer support
Three practical lessons emerge from this partnership for operators evaluating AI agent adoption:
Start with the right criterion. In an environment with an evolving regulatory framework and high user expectations, reliability is not optional. The first technology selection criterion should be response consistency, not cost or deployment speed.
Design for the diversity of your customer base. The iGaming sector serves a heterogeneous audience by nature. The system must adapt the support experience to the user's profile, not the other way around.
Treat AI as infrastructure, not a project. The best results come from operations that embed agents into the core of their strategy, with different levels of adoption across different contexts, rather than running them as a parallel layer alongside the main support operation.
Betano did not simply automate interactions. It built an operation that grows alongside the business, that knows the customer before the conversation begins, and that delivers trust with every interaction.
Want to understand how to structure memory-enabled AI agents in your operation? Book a demo →