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Snowflake Manufacturing Rotterdam, The Netherlands

IMCD

Enterprise Knowledge Search

simplify a complex, acquisition-heavy data estate so structured and unstructured product information could be governed centrally and reused for customer-facing AI experiences.

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52,000 different products
8 AI use cases developed in a single year
approximately 3,000 chemical suppliers

Reported impact

Business outcome

IMCD launched eight new AI use cases in a single year, improved product discovery and customer experience, and reduced administrative burden by standardizing data and governance across its global business.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth

Solution

IMCD uses Snowflake, Cortex Search, and connected enterprise systems such as Salesforce, Syndigo, and Hyland to build a governed commercial and AI data foundation. Structured ordering, finance, logistics, and customer data, plus large volumes of technical and regulatory documents, are unified in Snowflake so teams across regions and acquired businesses can work from the same scalable environment. Cortex Search adds grounded semantic retrieval over the catalog and document corpus, supporting natural-language discovery on IMCD’s website and B2B portal, while Snowflake-backed AI helps optimize tens of thousands of product descriptions for search. The broader platform standardizes governance and integration across core customer and product-information systems, reducing administration and making acquisitions easier to onboard while improving product discovery and customer service.

Data flow

Commercial, product, and document datasets from systems such as Salesforce, Syndigo, and Hyland are centralized in Snowflake. Cortex Search then indexes the grounded corpus for semantic retrieval, and curated search outputs are exposed to IMCD teams and customers through website and B2B portal experiences.

Solution architecture

5 components · 3 layers
  1. Ingest
    • Salesforce Supplies CRM and customer-engagement data that feeds the shared Snowflake foundation.
    • Syndigo Contributes product-information content used for IMCD’s catalog and search experiences.
    • Hyland Supplies document content that is searched and enriched through the Snowflake-based AI stack.
  2. Compute
    • Cortex Search Enables grounded semantic search across IMCD’s product and document corpus for teams and customers.
  3. Storage
    • Snowflake Provides the shared and governed data foundation for IMCD’s structured and unstructured commercial data.

Architecture clues

  • Cortex Search powers semantic retrieval for technical and regulatory documents across website and B2B experiences.
  • Snowflake has been integrated gradually since 2020 to create an open, secure, governed base for AI innovation.
  • The platform supports fast onboarding of newly acquired companies while keeping governance unified.

Evidence from the source

8 AI use cases developed in a single year
We’re using the whole suite of Cortex features, and that’s what enabled us to innovate so quickly
approximately 3,000 chemical suppliers
enable natural language search across its entire catalog
Transparency Evidence & methodology 75/100 reference value · 77% 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 confidence77%
  • 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 provenance0/10
    The publication date has no documented provenance.
  • 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. Snowflake

    IMCD customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/imcd/
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jun 18, 2025 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Jul 9, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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