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Snowflake Manufacturing Norderstedt, Germany

tesa

Predictive Maintenance

unify machine, API, ERP, and marketing data on one platform so tesa could detect manufacturing anomalies, simplify customer reporting, and experiment with generative AI without building its own stack.

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10 Terabytes of data managed
18 Systems integrated

Reported impact

Business outcome

tesa achieved 18 Systems integrated and 10 Terabytes of data managed.

  • Scalability

Solution

tesa uses Snowflake and Cortex AI on Snowflake to unify machine, API, ERP, and marketing data on one platform so tesa could detect manufacturing anomalies, simplify customer reporting, and experiment with generative AI without building its own stack. Machine, product, erp, and operational data are centralized in Snowflake so operations and engineering teams can work from a faster, more reliable operating view. Cortex AI adds in-platform LLM and agentic capabilities for classification, summarization, retrieval, or natural-language analytics on top of the curated data foundation. Keeping ingestion, storage, transformation, and governed access on one platform reduces handoffs, simplifies operations, and makes it easier to reuse trusted data across analytics, reporting, and AI. tesa achieved 18 Systems integrated and 10 Terabytes of data managed.

Data flow

machine, product, ERP, and operational data flow into Snowflake and Cortex AI; curated data, reports, and model outputs are then served to operations and engineering teams.

Solution architecture

2 components · 2 layers
  1. Compute
    • Cortex AI Provides in-platform AI functions used for summarization, classification, generation, or model-assisted analysis.
  2. Storage
    • Snowflake Provides the centralized data platform that stores governed datasets and serves analytics or AI workloads.

Architecture clues

  • Cortex AI adds in-platform LLM and agentic capabilities for classification, summarization, retrieval, or natural-language analytics on top of the curated data foundation.
  • Operates at the scale described by metrics such as 18 Systems integrated.

Evidence from the source

10 Terabytes of data managed
18 Systems integrated
But while the company has plenty of insights to work with, it wanted a new way to extract data from its manufacturing machines and integrate it with back-office systems
tesa achieved 18 Systems integrated and 10 Terabytes of data managed
tesa’s innovation finds a new home in the AI Data Cloud
Transparency Evidence & methodology 82/100 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 value82/100
  • 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 specificity12/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    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
    5 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

    tesa customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/tesa/
    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 Mar 22, 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 Jan 23, 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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