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Databricks Retail Switzerland

Coop

Data Lake / Data Warehouse Modernization · Enterprise Knowledge Search

Coop is a retailer with a unique mission: as a cooperative owned by over 4 million members, its goal isn't just profit, but feeding value back to its owners while prioritizing sustainability. To remain competitive during times of high inflation and shifting consumer habits, Coop needed to make data accessible to everyone, not just technical teams.

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30% Retention rate among internal users interacting with AskCap

Reported impact

Business outcome

Reported outcomes include 30% Retention rate among internal users interacting with AskCap.

  • Customer Experience

Solution

Coop is a retailer with a unique mission: as a cooperative owned by over 4 million members, its goal isn't just profit, but feeding value back to its owners while prioritizing sustainability. To remain competitive during times of high inflation and shifting consumer habits, Coop needed to make data accessible to everyone, not just technical teams. By leveraging the Databricks Data + AI Platform, Coop transformed an internal hackathon idea into "AskCap", an AI-powered assistant that allows employees to query enterprise data via Microsoft Teams using natural language. Turning a hackathon project into a trusted business tool required a robust engine. AskCap acts as a "meta layer" built on top of the Databricks Data + AI Platform, utilizing Databricks Genie for its native natural language capabilities. The bot is integrated directly into Microsoft Teams, meeting users where they already work.

Data flow

Operational, customer, and transactional data lands in Databricks, is transformed in Delta Lake tables under Unity Catalog governance, and is served to downstream users and applications through Genie, Microsoft Teams.

Solution architecture

5 components · 4 layers
  1. Compute
    • Databricks Data + AI Platform Provides the shared lakehouse environment for ingestion, engineering, analytics, and AI workloads.
  2. Storage
    • Delta Lake Stores curated lakehouse tables used for reliable downstream analytics and AI workloads.
  3. Serving
    • Genie Lets business users ask natural-language questions over governed enterprise data.
    • Microsoft Teams Acts as the conversational delivery channel for the data assistant experience.
  4. Governance
    • Unity Catalog Applies centralized governance, lineage, and access controls across shared data assets.

Architecture clues

  • Natural-language analytics is exposed through Genie.
  • Runs on Azure.
  • Uses Unity Catalog for centralized governance and access control.

Evidence from the source

30% Retention rate among internal users interacting with AskCap
By leveraging the Databricks Data + AI Platform, Coop transformed an internal hackathon idea into "AskCap", an AI-powered assistant that allows employees to query enterprise data via Microsoft Teams using natural language.
Coop is a retailer with a unique mission: as a cooperative owned by over 4 million members, its goal isn't just profit, but feeding value back to its owners while prioritizing sustainability.
The journey to democratizing data didn't start with a top-down mandate; it started at Coop's annual internal hackathon.
To remain competitive during times of high inflation and shifting consumer habits, Coop needed to make data accessible to everyone, not just technical teams.
Transparency Evidence & methodology 86/100 reference value · 92% 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 value86/100
  • 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 impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
  • 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
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency9/10
    2 source references 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. Databricks

    Coop customer story

    First-party
    https://www.databricks.com/customers/coop
    Source type
    Vendor-published customer story
    Published
    Feb 16, 2026
    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. Coop

    Annual Report 2025 imprint

    First-party
    https://report.coop.ch/en/imprint/

    The official imprint lists "Coop Cooperative" at "Thiersteinerallee 12" and "4002 Basel," supporting Switzerland as the headquarters country.

    Source type
    Official company website
    Supports
    • Region

Date evidence

  • Published on source page Feb 16, 2026 · Databricks · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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