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IBM Retail Germany

EDEKA Rhein-Ruhr Stiftung & Co. KG

Enterprise Knowledge Search · IT Operations Automation

EDEKA's internal support teams had to search across manuals, technical documentation and other formats to resolve software and store-system issues, making problem solving slow and operational support inefficient.

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ROI of the solution is less than three months

Reported impact

Business outcome

EDEKA dramatically reduced support problem-solving time and achieved a pilot ROI of less than three months, demonstrating that generative retrieval can streamline internal IT operations without forcing teams back into manual document review.

  • Cost Reduction
  • Productivity Improvement

Solution

EDEKA worked with IBM Client Engineering and IBM Expert Labs on a pilot that turned internal support documentation into a generative-AI knowledge workflow. Manuals, technical documents and other support sources were ingested into IBM Watson Discovery to build a searchable knowledge base. When a support user submits a question, Discovery retrieves the most relevant passages and IBM watsonx.ai uses that evidence plus the user query to generate a grounded answer. A custom application built with the Vue Framework exposes the experience through a search box, answer panel and linked supporting passages so staff can both read the synthesized answer and inspect the source text behind it. The two-phase design first organized the document estate and then layered generative AI on top of retrieval, cutting troubleshooting effort and producing a pilot with payback in under three months.

Data flow

Support manuals and technical documents are ingested into Watson Discovery, user queries are submitted through the Vue application, Discovery returns relevant passages, and watsonx.ai generates an answer that is shown alongside the original evidence.

Solution architecture

3 components · 3 layers
  1. Ingest
    • IBM Watson Discovery Ingests EDEKA's support manuals and technical documentation and retrieves the passages used to answer internal support questions.
  2. Compute
    • IBM watsonx.ai Generates natural-language answers from the retrieved evidence so staff can resolve support issues faster.
  3. Serving
    • Vue Framework Hosts the custom front end that accepts user queries and displays answers, source passages and related documents.

Architecture clues

  • The custom application surfaced both answers and the underlying relevant passages for traceability.
  • The pilot focused on software systems used in EDEKA stores.
  • The rollout happened in two phases: first document structuring in Discovery, then answer generation with watsonx.ai.

Evidence from the source

manual documents, technical documentations and other sources, which were ingested by the Watson Discovery service
problem-solving time fell dramatically
the ROI of the solution is less than three months
used the combination of IBM Watson Discovery IBM watsonx.ai
Transparency Evidence & methodology 75/100 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 value75/100
  • 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 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 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
    4 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 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. IBM

    EDEKA Rhein-Ruhr Stiftung & Co. KG customer story

    First-party
    https://www.ibm.com/case-studies/edeka
    Source type
    Vendor-published customer story
    Published
    Apr 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

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

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