Banco Bradesco

Banco Bradesco, one of Latin America's largest financial institutions serving approximately 74 million customers, faced compounding pressure from Brazil's rapidly evolving regulatory environment, escalating fraud and cyber threats, and rising consumer expectations for smooth, personalized banking across WhatsApp, mobile apps, and phone. Internally, fragmented approval workflows and technical barriers delayed the release of new AI-powered solutions, while manual tasks and decentralized knowledge bases prevented employees from focusing on higher-value work. Without a unified AI platform, the bank risked deteriorating service quality, rising operational costs, and slower compliance response — all in an increasingly digital-first competitive landscape.

Bradesco partnered with Microsoft and Avanade to build Bridge, a multi-agent, technology-agnostic generative AI platform powered by Azure OpenAI in Foundry Models. Bridge comprises four specialized agents: BIA for Customers (serving ~74 million retail customers across digital channels), BIA Corporate (an employee assistant backed by Azure Cosmos DB for real-time knowledge retrieval), MentorIA (automated analysis of collections calls), and an Audit Intelligent Learning Assistant. Azure API Management governs deployment and scaling across all channels, while Azure Red Hat OpenShift provides containerized workload management with enterprise-grade content safety, prompt management, and agent intent classification. Microsoft Power Platform and low-code interfaces allow non-technical business teams to create and manage their own agents — removing the data-scientist bottleneck — while Azure Redis Cache sustains performance across services processing more than 2 million requests and 2 billion inference tokens daily.

Bridge delivered measurable impact across customer-facing and internal operations. BIA for Customers achieved an 83% digital resolution rate, while BIA Corporate resolved 80% of employee queries without escalation and drove a 6-point NPS increase. The platform retains 89% of requests fully within the AI layer. Product launches are up to 10x faster through low-code tooling and streamlined approval flows. Additional outcomes include:

• MentorIA automates 18,000+ collections calls per day, yielding a 22%+ increase in conversion
• Audit planning efficiency improved 65%
• Managerial productivity increased 8x
• Infrastructure automation delivered a 30%+ reduction in technology costs

• Multi-agent architectures let a single platform address customer service, employee productivity, collections, and compliance without duplicating infrastructure or governance overhead.
• Embedding AI governance — content safety, prompt management, intent classification — at the architecture level is non-negotiable for compliant financial services deployment at scale.
• Integrating AI into high-adoption channels like WhatsApp drives substantially higher containment and resolution rates than standalone chatbot deployments.
• Low-code interfaces that empower non-technical teams to build and manage agents are a prerequisite for enterprise-scale AI adoption, not an optional enhancement.
• Purpose-built data infrastructure must be co-designed with the AI layer: processing 2 billion inference tokens daily without bottlenecks requires deliberate infrastructure choices from the start.