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AWS Commercial and Professional Services Global

Mercedes-Benz Consulting

Application Refactoring · Document Processing · Enterprise Knowledge Search · IT Operations Automation · Real-time Analytics · System Migration

With millions of internal documents to search through, getting relevant answers quickly was a standing challenge for employees. Additionally, if an employee left the company or a colleague with specific knowledge was not available, the only way to find the needed information was within these documents.

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Reported impact

Business outcome

Mercedes-Benz Consulting is in the process of developing innovative solutions for multiple internal clients using mature AI technology that is flexible, scalable, and secure. Although the new application is already in use within Mercedes-Benz Consulting, the company wants to expand its solution for use in workshops and on showroom floors. “With this AWS engagement and everything we’ve developed in the past year, we have moved our focus from doing projects to creating products,” says Dr. Chadha. “We have learned to build in a flexible, scalable manner so that we can incorporate user requirements and develop our system further.” AWS Professional Services played a crucial role in professionalizing our cloud, data, and ML infrastructure on AWS.

  • Cost Reduction
  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Mercedes-Benz Consulting , a subsidiary of the Mercedes-Benz Group AG, wanted to empower its employees by giving them better access to company and project information. Whether they are working on a client project or internally, Mercedes-Benz Consulting employees often need to obtain answers quickly from sources containing millions of technical documents from various source systems, and searching these needs to be as simple as possible. To automate the migration of data, Mercedes-Benz Consulting uses AWS Lambda , a serverless, event-driven compute service. For its ML model development, monitoring, and deployment, Mercedes-Benz Consulting uses Amazon SageMaker , a service to build, train, and deploy ML models. This data is then processed and cleaned using AWS Glue , a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources. These data embeddings are then accessed by Amazon

Data flow

Data or events enter through AWS Glue; processing runs on AWS Lambda, Amazon SageMaker; and outputs are exposed through Amazon OpenSearch Service for the digital assistant workload.

Solution architecture

4 components · 3 layers
  1. Ingest
    • AWS Glue Ingests or moves data and events into the digital assistant pipeline.
  2. Compute
    • AWS Lambda Runs core compute or model execution for the digital assistant workload.
    • Amazon SageMaker Runs core compute or model execution for the digital assistant workload.
  3. Serving
    • Amazon OpenSearch Service Runs core compute or model execution for the digital assistant workload.

Architecture clues

  • Developers can work more efficiently using the AI assistant, and searching for information by asking questions in natural language eases workflow friction across the company.
  • Mercedes-Benz Consulting needed to architect a strong foundation for its data sources, and it took a serverless-first approach.
  • This data is then processed and cleaned using AWS Glue , a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources.
  • To automate the migration of data, Mercedes-Benz Consulting uses AWS Lambda , a serverless, event-driven compute service.

Evidence from the source

Additionally, if an employee left the company or a colleague with specific knowledge was not available, the only way to find the needed information was within these documents.
Mercedes-Benz Consulting needed to architect a strong foundation for its data sources, and it took a serverless-first approach.
This data is then processed and cleaned using AWS Glue , a serverless data integration service that makes it simple to discover, prepare, move, and integrate data from multiple sources.
To automate the migration of data, Mercedes-Benz Consulting uses AWS Lambda , a serverless, event-driven compute service.
When employees ask a question of the AI solution, the process starts with data going through the extract, transform, load process.
With millions of internal documents to search through, getting relevant answers quickly was a standing challenge for employees.
Transparency Evidence & methodology 66/100 reference value · 88% 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 value66/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence88%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 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

    Mercedes-Benz Consulting customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/mercedes-benz-consulting/
    Source type
    Vendor-published customer story
    Published
    Mar 5, 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. LinkedIn

    Mercedes-Benz Consulting GmbH | LinkedIn

    Independent secondary
    https://www.linkedin.com/company/mercedes-benz-consulting-gmbh/

    The Organization JSON-LD lists addressLocality 'Leinfelden-Echterdingen', and addressCountry 'DE', and numberOfEmployees value 332.

    Source type
    Structured company profile
    Supports
    • Company size

Date evidence

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

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