Microsoft Education Brazil

SEDUC São Paulo

Customer Service Automation · Enterprise Knowledge Search · IT Operations Automation

SEDUC has an overwhelming amount of student essays. They required personalized content creation. They wanted to decentralize access to data.

And if you have about 40 students in each of these classes, we are talking about 240 students.
Azure OpenAI Service analyzes millions of essays, offering personalized, tailored feedback to educators so they can help students improve their writing skills and understanding of the subject matter.
SEDUC is rolling out Microsoft 365 A5 licenses to all employees, educators, and 3.5 million students to enhance security governance and provide improved support.
São Paulo hosts Brazil’s largest education system, with over 3.7 million students across more than five thousand schools.

Reported impact

Business outcome

By leveraging Microsoft AI-powered solutions and committing to responsible AI practices, SEDUC has improved student outcomes, enhanced efficiency, and addressed key educational challenges. As AI continues to evolve, SEDUC’s proactive approach to integrating these technologies positions it as a leader not just in Brazil but globally.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

SEDUC São Paulo built this solution on Azure OpenAI Service, Power Platform, Dynamics 365. SEDUC has an overwhelming amount of student essays. They required personalized content creation. They wanted to decentralize access to data. Azure OpenAI Service provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow. Power Platform provides the governed low-code foundation for apps, automations, and copilots. Dynamics 365 provides the core business application experience for service, sales, or operations workflows. SEDUC leveraged Azure OpenAI Service for personalized feedback on student essays and automated content creation, reduced workload on educators, and improved student outcomes. Using Microsoft's responsible AI standards, SEDUC has implemented trustworthy AI development, ensuring data security, ethical considerations, and transparency.

Data flow

Messages, calls, or requests enter the existing workflow, Azure OpenAI Service classifies or summarizes them, and the reviewed output is surfaced to agents and frontline staff.

Solution architecture

3 components · 3 layers
  1. Compute
    • Azure OpenAI Service Provides the generative AI model layer used for summarization, drafting, classification, or copilots in the workflow.
  2. Serving
    • Dynamics 365 Provides the core business application experience for service, sales, or operations workflows.
  3. Orchestration
    • Power Platform Provides the governed low-code foundation for apps, automations, and copilots.

Architecture clues

  • The implementation relies on Azure OpenAI Service as a core technical building block.

Evidence from the source

By leveraging Microsoft AI-powered solutions and committing to responsible AI practices, SEDUC has improved student outcomes, enhanced efficiency, and addressed key educational challenges.
SEDUC has an overwhelming amount of student essays. They required personalized content creation. They wanted to decentralize access to data.
SEDUC is rolling out Microsoft 365 A5 licenses to all employees, educators, and 3.5 million students to enhance security governance and provide improved support.
SEDUC leveraged Azure OpenAI Service for personalized feedback on student essays and automated content creation, reduced workload on educators, and improved student outcomes.
São Paulo hosts Brazil’s largest education system, with over 3.7 million students across more than five thousand schools.
Transparency Evidence & methodology 80/100 reference value · 89% 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 value80/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    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. Microsoft

    SEDUC São Paulo customer story

    First-party
    https://www.microsoft.com/en/customers/story/20134-seduc-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Dec 6, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Dec 6, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.