AWS Consumer Goods Brazil

Grendene

Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search · Marketing Personalization · Sales Automation

Grendene's open innovation analyst, Bianca Nascimento, explains that Charla is being fed with the descriptions of Melissa's last four collections. “She will also receive information about the sales behavior of two of the Melissa clubs, which will allow us to test the assistant in a controlled environment so that it can later be scaled,” she says.

complementary and cross sales Increasing
highly personalized recommendations to consumers Providing
inventory trends and strategies Identifying
the digital experience to the 403 Melissa clubs operating in Brazil Taking

Reported impact

Business outcome

Grendene's digital business director, Daniel Gandolfi, explains that due to the brand's large number of sales points, Melissa was chosen for the development of the company's first generative artificial intelligence (AI)-based sales force support solution. “The goal is to increase this team’s operational gain, reducing the traditional walks to the stock, speeding up inquiries and allowing it to offer consumers the best complement to the product they are purchasing, all of this with information that helps sales conversion,” he explains, stressing that the use of AI also reinforces Grendene's position as a company that is not afraid of testing new technologies and bringing solutions so that its partners and franchisees have good results.

  • Customer Experience

Solution

Innovating its sales processes, Grendene is creating a generative AI-based virtual assistant for the Melissa clubs’ sales team with the support of AWS and MadeinWeb. Finally, Duarte also highlights the possibility of identifying inventory trends and strategies through customer buying pattern and behavior analysis. “It will be possible to identify which products are most likely to be sold, allowing the store to direct its resources to inventory and marketing strategies for products that are in high demand and following current market trends,” he explains. An example is the strategic inventory knowledge and smart redirection, where Charla, using detailed inventory data from the entire network, can offer suggestions for alternative products in the event of a local stock-out, or direct customers to other channels and stores in the chain, using an omnichannel approach. “We also expect an increase in complementary and cross sales, since the assistant

Data flow

processing runs on Amazon SageMaker, Amazon EC2, Amazon Bedrock; results and state are stored in Amazon S3 for the AI assistant workload.

Solution architecture

4 components · 2 layers
  1. Compute
    • Amazon SageMaker Runs core compute or model execution for the AI assistant workload.
    • Amazon EC2 Runs core compute or model execution for the AI assistant workload.
    • Amazon Bedrock Runs core compute or model execution for the AI assistant workload.
  2. Storage
    • Amazon S3 Stores application, analytics, or workflow state for the AI assistant solution.

Evidence from the source

For the head of retail tech Melissa, Walter Duarte, these recommendations will bring benefits to employees on several fronts.
Gandolfi also points out that the initiative is one of the company's first to bring technological solutions, common in B2C relationships, to B2B relations as well. “E-commerce usually provides recommendations for the end customer.
Grendene's open innovation analyst, Bianca Nascimento, explains that Charla is being fed with the descriptions of Melissa's last four collections. “She will also receive information about the sales behavior of two of the Melissa clubs, which will allow us to test the assistant in a controlled environment so that it can later be scaled,” she says.
Tremarim believes that the introduction of new technologies tends to make results appear more quickly. “With this joint movement with AWS, we tend to gain a lot in the assertiveness of what consumers will receive, not only with what they order, but also regarding other products that may be offered together,” he says.
We are talking about a value chain, made up of franchisees and sellers who also need to receive these recommendations,” he says.
When the testing phase is complete, the new virtual assistant will be made available to the sales teams of 403 active Melissa clubs in Brazil, the vast majority of which are installed in shopping centers.
Transparency Evidence & methodology 65/100 reference value · 90% confidence · 2 sources

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 value65/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence90%
  • 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
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    2 source references 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

    Grendene customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/grendene-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 9, 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
  2. MarketScreener

    Company Grendene S.A.

    Independent secondary
    https://www.marketscreener.com/quote/stock/GRENDENE-S-A-6497540/company/

    Grendene SA is a Brazil-based company ... Number of employees: 16,378.

    Source type
    Structured company profile
    Supports
    • Company size
    • Region

Date evidence

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