Microsoft Retail France

Cdiscount

Customer Service Automation · Enterprise Knowledge Search · Security and Compliance

Riding the wave of disruptive technologies "Generative AI represents a real technological breakthrough that we need to seize," asserts Nicolas Thibaut, pointing out that within Cdiscount, the potential of this technology is unanimously supported, first and foremost, by management.

20% for junior profiles.
Creating a new-generation customer experience With its 17 million unique visitors per month and 70 million product sheets, Cdiscount has an unrivaled...
With no less than 70 million products, 9 million customers
With no less than 70 million products, 9 million customers, and some 14,000 sellers, the Cdiscount marketplace has been a key player in French e-commerce for...

Reported impact

Business outcome

Team commitment, the cornerstone of innovation While these initial experiments have been conclusive, the adoption of generative AI cannot be achieved without the full involvement of the teams: "We have carried out a major effort to raise awareness of the tool, increasing the number of users from 100 to 290," Nicolas Thibaut says, pleased.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

Cdiscount used Azure OpenAI Service, Azure AI Foundry, GitHub Copilot to replace disconnected manual workflows with a more consistent digital operating model. Low-code automation and copilot capabilities orchestrate tasks, route work, and surface context to agents or business users without requiring separate point solutions. Where generative AI is involved, Azure OpenAI handles the language understanding layer while connected data and workflow services keep responses grounded in enterprise context. It also connects with surrounding systems such as GitHub Copilot, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. Team commitment, the cornerstone of innovation While these initial experiments have been conclusive, the adoption of generative AI cannot be achieved without the full involvement of the teams: "We have carried out a major effort to raise awareness of the tool, increasing the number of users from 100.

Data flow

Content and operational context are prepared in the application data layer, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

4 components · 3 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
  2. Serving
    • GitHub Copilot Accelerates software development by assisting engineers with code generation and editing.
  3. Orchestration
    • Azure AI Foundry Provides the managed environment for model orchestration, experimentation, and responsible AI operations.
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • 9 million customers
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

"Thanks to GitHub Copilot, they save an average of half a working day a week." More concretely, the pull request rate—enabling a developer to inform his team members that the newly developed functionality is available for revision and...
In fact, the French e-commerce leader has already begun its deployment across all business lines: "We want all our employees to have access to generative AI, and to be able to use it to develop business and professional ideas." This high...
Making GitHub Copilot an ally for developers A fortnight later, the tool has already been adopted by around 100 developers, who use it to generate algorithms, frameworks, and tests in JavaScript, React, Python, and .NET Core, so that they...
Riding the wave of disruptive technologies "Generative AI represents a real technological breakthrough that we need to seize," asserts Nicolas Thibaut, pointing out that within Cdiscount, the potential of this technology is unanimously...
Team commitment, the cornerstone of innovation While these initial experiments have been conclusive, the adoption of generative AI cannot be achieved without the full involvement of the teams: "We have carried out a major effort to raise...
Transparency Evidence & methodology 96/100 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 value96/100
  • 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 specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    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

    Cdiscount customer story

    First-party
    https://www.microsoft.com/en/customers/story/1785788806559352474-cdiscount-azure-openai-service-retailers-en-france
    Source type
    Vendor-published customer story
    Published
    Jul 4, 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 Jul 4, 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.