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AWS Consumer Goods Brazil

Condor

IT Operations Automation

Condor, a Brazilian company in the personal and household care accessories sector, developed a generative AI prototype to serve its information technology area. The project used AWS services, with support from AWS Partner MadeinWeb, creator of the Charla generative AI solution.

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90 percent

Reported impact

Business outcome

Condor, a Brazilian company in the personal and household care accessories sector, developed a generative AI prototype to serve its information technology area. The project used AWS services, with support from AWS Partner MadeinWeb, creator of the Charla generative AI solution.

Solution

Condor, a Brazilian company in the personal and household care accessories sector, developed a generative AI prototype to serve its information technology area. The project used AWS services, with support from AWS Partner MadeinWeb, creator of the Charla generative AI solution. Pereira recalls that the project resulted in the prototype of an IT service model integrated with the knowledge base of the area and the company's call history. “Using this chatbot, we had the idea of supplementing service desk activities with the adoption of generative AI,” he says. It's basically the creation of an interface where employees can find answers to their questions based on the company's IT ticket history. Charla also helps direct integration into company tools, such as the calling tool, as it is a service built with APIs.

Data flow

Data is stored in Amazon S3, Amazon OpenSearch Service and processed by Amazon Bedrock, Amazon SageMaker.

Solution architecture

6 components · 3 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
    • Amazon SageMaker Supports model training, tuning, deployment, or managed ML inference.
    • AWS Lambda Runs event-driven application logic and serverless processing tasks.
  2. Storage
    • Amazon S3 Stores documents, media, telemetry, or training data durably at scale.
    • Amazon OpenSearch Service Stores and retrieves indexed or vectorized content for search and RAG workflows.
  3. Serving
    • Amazon API Gateway Exposes application and AI services through managed APIs.

Architecture clues

  • Native integration with Amazon S3 objects made it easy to implement the Amazon OpenSearch Service to create a fully vectorized database, allowing a more efficient search and advanc
  • “Using this chatbot, we had the idea of supplementing service desk activities with the adoption of generative AI,” he says.

Evidence from the source

Condor, a Brazilian company in the personal and household care accessories sector, developed a generative AI prototype to serve its information technology area.
The project used AWS services, with support from AWS Partner MadeinWeb, creator of the Charla generative AI solution.
Transparency Evidence & methodology 62/100 reference value · 75% 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 value62/100
  • 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 specificity12/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence75%
  • 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 evidence10/20
    2 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    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. AWS

    Condor customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/condor-generative-ai/
    Source type
    Vendor-published customer story
    Published
    Feb 22, 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

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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