What was the impact of the AI solution?

BRIDGE now supports more than 500 active generative AI initiatives. The bank achieved a 10-fold reduction in AI solution integration cycles. It achieved a 10-fold increase in agility in delivering informational agents. The platform supports a 79% resolution rate in internal support. It drove a 37% acceleration in development. It drove a 64% increase in user story creation efficiency. The bank performs more than 1,000 automated code reviews each month. More than 10 million customers have engaged across 25 million interactions using generative AI, achieving an 87% resolution rate.

Source

MicrosoftScaled productionEvidence: Medium65/100

Banco Bradesco builds BRIDGE generative AI platform on Azure Red Hat OpenShift connected to Azure OpenAI for multi-agent initiatives

Use case typeWorkflow automationUpdated Jun 15, 2026

Banco Bradesco, one of Brazil's largest private financial groups, built a scalable generative AI platform called BRIDGE on Microsoft Azure Red Hat OpenShift. The platform integrates multiple AI models, supports modular multi-agent creation for business teams, and connects to Azure OpenAI while embedding governance, security, and regulatory controls. BRIDGE serves internal support, development, operations, technology, and customer-service use cases across the bank.

Organization: Bradesco
Industry: Finance
Location: Brazil
Published: June 2026

Reported outcomes

+64%

user story creation efficiencyProductivity & throughput

500 initiativesactive generative AI initiatives−90%AI solution integration cycles+900%agility in delivering informational agents79%internal support resolution rate37%development acceleration

Strategic outcomes

Risk & complianceScaled generative AI in a regulated environmentSpeed & agilityFaster launch of new generative AI projectsCustomer experience & trustMore personalized customer servicesInnovation & cultureStrengthened a culture of innovationScale & capacityEnterprise-wide AI platform for multiple business areas

Catalog median for productivity & throughput deployments: +40% across 108 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Active generative AI initiatives: 500 initiatives increase

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"more than 500 active generative AI initiatives"

Normalized claim

AI solution integration cycles: 90% decrease

Red Hat success storyJun 15, 2026Customer storyInferred claimMedium evidence strength

"10-fold reduction in AI solution integration cycles"

Normalized claim

Agility in delivering informational agents: 900% increase

Red Hat success storyJun 15, 2026Customer storyInferred claimMedium evidence strength

"10-fold increase in agility in delivering informational agents"

Normalized claim

Internal support resolution rate: 79%

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"a 79% resolution rate in internal support"

Normalized claim

Development acceleration: 37%

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"a 37% acceleration in development"

Normalized claim

User story creation efficiency: 64% increase

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"a 64% increase in user story creation efficiency"

Normalized claim

Automated code reviews: 1,000 reviews/month increase

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"more than 1,000 automated code reviews each month"

Normalized claim

Customer interactions: 25,000,000 interactions

Red Hat success storyJun 15, 2026Customer storyExplicit claimMedium evidence strength

"across 25 million interactions"

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Bradesco
Provider: Microsoft
Maturity: Scaled Production
Linked source: Red Hat success story

Banco Bradesco needed a scalable, governed generative AI foundation that could integrate multiple models and vendors, orchestrate microservices at scale, meet strict regulatory and data governance requirements, and accelerate time to market

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 1 of 1

1Workflow automation

The bank developed BRIDGE, a unified generative AI platform built on Microsoft Azure Red Hat OpenShift.
The platform orchestrates containerized AI microservices with high availability and built-in security-focused features.
BRIDGE supports simple text generation through multi-agent systems and integrates multiple LLMs, including Azure OpenAI.
The modular architecture lets business teams create and manage AI agents with minimal technical complexity while maintaining governance and compliance.

Technologies

Microsoft Azure Red Hat OpenShift Azure OpenAI

BRIDGE now supports more than 500 active generative AI initiatives.
The bank achieved a 10-fold reduction in AI solution integration cycles.
It achieved a 10-fold increase in agility in delivering informational agents.
The platform supports a 79% resolution rate in internal support.
It drove a 37% acceleration in development.
It drove a 64% increase in user story creation efficiency.
The bank performs more than 1,000 automated code reviews each month.
More than 10 million customers have engaged across 25 million interactions using generative AI, achieving an 87% resolution rate.

Architecture

BRIDGE is a unified generative AI platform running on Microsoft Azure Red Hat OpenShift. It orchestrates containerized AI microservices, supports multiple large language models, and connects to Azure OpenAI and other external models (including OpenAI, Google, and Meta) under centralized governance and regulatory controls.

Implementation partners1

Red Hat

Sources & evidence1

Evidence: Medium65/100Evidence strength