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AWS Automotive Italy

Ferrari

IT Operations Automation · Marketing Personalization

Ferrari remains committed to furthering its technology road map with generative AI , seeing it as an opportunity to improve the vehicle and customer journeys.

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The company is also exploring cloud services that will help it meet its carbon neutrality commitment by 2030.
To assist its sales professionals and technicians, the company fine-tuned LLMs in Amazon Bedrock—including Amazon Titan , Claude 3, and Llama—on its documentation.

Reported impact

Business outcome

With a strong cloud foundation on AWS, Ferrari used generative AI solutions to gain measurable impacts across its business. Ferrari will continue expanding its use of generative AI to better serve customers and dealers. Key reported metrics include The company is also exploring cloud services that will help it meet its carbon neutrality commitment by 2030.; To assist its sales professionals and technicians, the company fine-tuned LLMs in Amazon Bedrock—including Amazon Titan , Claude 3, and Llama—on its documentation..

  • Productivity Improvement
  • Revenue Growth

Solution

Learn how Ferrari uses generative AI on AWS to enhance the customer and vehicle journeys to increase sales, experimentation, and productivity. Luxury Italian auto manufacturer Ferrari S.p.A. (Ferrari) has forged a globally recognized legacy that’s rooted in tradition and innovation over many decades. To continue to deliver the best possible experiences to customers and dealers, Ferrari has tapped into the power of generative artificial intelligence (AI) by turning to Amazon Web Services (AWS). Ferrari has applied generative AI to several use cases, from accelerating the vehicle design process to providing personalized services to its customers. “Every day, we try to find new ways of achieving excellence while improving our customers’ experiences and their connection with our brand,” says Silvia Gabrielli, chief digital and data officer at Ferrari.

Data flow

Source data enters the Ferrari workflow through its application services, processed with AWS Fargate, Amazon SageMaker JumpStart, Amazon SageMaker, and delivered through Amazon Personalize.

Solution architecture

7 components · 2 layers
  1. Compute
    • AWS Fargate Runs container tasks without managing servers.
    • Amazon SageMaker JumpStart Accelerates prototyping with prebuilt models and starter assets.
    • Amazon SageMaker Supports model training, fine-tuning, evaluation, or managed inference for the customer workload.
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.
    • Amazon Titan Provides Amazon foundation models used for embeddings, text, or image generation tasks.
    • Claude 3 Serves as the primary large language model for generation or analysis tasks.
  2. Serving
    • Amazon Personalize Generates personalized recommendations for end users.

Architecture clues

  • “We can connect to a single layer of APIs to quickly test, benchmark, and deploy different models.” Ferrari combines its use of Amazon Bedrock with Amazon SageMaker JumpStart , an ML hub with foundation models, built-in

Evidence from the source

Ferrari remains committed to furthering its technology road map with generative AI , seeing it as an opportunity to improve the vehicle and customer journeys.
The company is also exploring cloud services that will help it meet its carbon neutrality commitment by 2030.
To assist its sales professionals and technicians, the company fine-tuned LLMs in Amazon Bedrock—including Amazon Titan , Claude 3, and Llama—on its documentation.
To bring the luxury experience to customers around the world, Ferrari developed the car configurator on AWS, giving its customers the ability to personalize their own Ferrari, from wheel selection to paint colors to interior options.
With a strong cloud foundation on AWS, Ferrari used generative AI solutions to gain measurable impacts across its business.
“There are millions of possible configurations,” says Simone Canditone, digital experience manager at Ferrari.
Transparency Evidence & methodology 91/100 reference value · 87% 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 value91/100
  • 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 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 confidence87%
  • 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
    6 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 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. AWS

    Ferrari customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/ferrari-generative-ai-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 1, 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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