Use Case Intelligence RSS Search use cases Search
Bain Financial Services Brazil

Bradesco

Customer Service Automation

Bradesco wanted to move beyond an early AI assistant and scale generative AI across the bank, but it needed a shared technical foundation, trained teams, and production use cases that could improve both employee and customer experiences quickly.

Read the original story Explore all use cases
12 weeks to integrate generative AI into the branch manager chatbot
40% reduction in average call handling time in pilot
90% retention rate of customer chatbot
95% response accuracy of customer chatbot

Reported impact

Business outcome

Bradesco used the shared platform to launch customer-facing and agent-assist tools quickly, achieving strong chatbot retention and response accuracy while reducing call handling time and expanding generative AI across the bank.

  • Customer Experience
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Bradesco used Bain support to turn an internal chatbot upgrade into a broader generative AI rollout. The bank first improved its branch manager chatbot in 12 weeks, then used that success to stand up its first LLM-agnostic generative AI platform so future applications could scale without locking the bank to one model. On top of that platform, Bradesco built three major experiences: a customer chatbot that answers questions and resolves issues for millions of customers, a call center chatbot that assists service agents during support interactions, and Smart payments, an agentic payment experience delivered through WhatsApp. The bank paired the shared platform with cross-functional training across technical, business, and customer-experience teams. The source does not name a separate storage tier, but it clearly describes a reusable platform layer that routes customer queries, market context, and payment requests into customer-facing and agent-assist workflows.

Data flow

Bradesco routes internal branch-manager questions, customer requests, and payment intents into a shared generative AI platform. That platform feeds the customer chatbot, the call center chatbot, and the Smart payments experience on WhatsApp, while trained business and technical teams refine the apps and support production rollout.

Solution architecture

5 components · 2 layers
  1. Serving
    • customer chatbot Handles customer questions and personalized support without routing every interaction to a human agent.
    • call center chatbot Supports customer service agents by accelerating inquiry resolution and shortening handling time.
    • Smart payments Extends the platform into agentic payments with conversational money transfers and richer transaction assistance.
    • WhatsApp Acts as the delivery channel for Bradesco's conversational payment experience.
  2. Orchestration
    • generative AI platform Provides Bradesco's shared, LLM-agnostic foundation for building and scaling multiple generative AI applications.

Architecture clues

  • Bradesco's first generative AI platform was designed to be LLM-agnostic.
  • The bank trained technical, business, and customer experience teams for enterprise-wide readiness.
  • Three distinct applications were built on the same shared platform foundation.

Evidence from the source

"Built in just eight weeks, it exceeded its 50% retention goal by achieving 90%"
"In just 12 weeks, the bank successfully integrated generative AI"
"The chatbot responds to customer queries and provides personalized support."
"We helped Bradesco create its first generative AI platform, designed to be LLM-agnostic"
"reduces average call handling time by 40% (in pilot)"
Transparency Evidence & methodology 100/100 reference value · 85% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability25/25
    12 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • Source transparency10/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Bain & Company

    A Big Bet on Generative AI Puts Bradesco Ahead of the Curve | Bain & Company

    First-party
    https://www.bain.com/client-results/ai/bradesco/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Industry
    • Region
    • Company size
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • Business outcome
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jul 10, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Apr 27, 2026 · Bain · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source

Related

Similar use cases

See all like this
Bain Nov 22, 2024 Banca Investis Financial Services Italy Banca Investis wanted to modernize its service model with more frequent, higher-quality customer interactions, and it needed an AI system that could personalize investment conversations while giving bankers timely… 500+ pieces of information and research analyzed daily Customer Service AutomationMarketing Personalization NIWA app · chatbot OpenAI Oct 16, 2026 Itaú Financial Services Latin America Itaú needed to make financial guidance simpler and more personalized for millions of customers while also scaling document-heavy internal workflows, all under the governance, observability, and economic controls… 100% of the customer base is the goal by the end of 2026 Customer Service AutomationDocument Processing Customer DNA · IARA · OpenAI models Google Cloud Modified · Oct 9, 2026 Nova Gestões Financial Services Brazil Nova Gestões used Gemini to eliminate manual data entry and boost agent productivity by 30% across 100 million monthly calls. 30% productivity increase across 1,700 agents Customer Service Automation Gemini models · Gemini platform