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Snowflake Energy Düsseldorf, Germany

Uniper

Predictive Maintenance · Risk Management

Volatile energy markets demanded faster access to trading, risk, and operational data than Uniper's aging reporting platform and fragmented systems could provide.

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18.5 gigawatts of power-generating capacity
approximately 7,000 employees
migration to Snowflake took just a little over a year

Reported impact

Business outcome

Uniper improved productivity and resilience, delivered key data before business hours, removed downtime from huge queries, and uses AI agents to save countless hours on critical operational tasks.

  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

Uniper built a central data and AI foundation on Snowflake to consolidate trading, risk, pricing, optimization, and power-generation data and make it useful at business speed. The company migrated a decade-old reporting platform into Snowflake, giving teams cloud scalability without the storage constraints and upkeep that previously consumed engineering capacity. Key data sets now land on one platform, and start-of-day reports are available earlier so decision-makers can react to market shifts and operational issues during business hours rather than after long batch delays. On top of that governed backbone, Uniper uses Snowflake CoCo and Snowflake CoWork to develop AI agents that combine safety instructions, security guidance, failure history, market prices, and maintenance schedules to recommend the best times and procedures for UK power-plant maintenance. Employees also use CoCo to diagnose non-performant SQL and enable more self-service optimization.

Data flow

Trading, price, risk, and operational data flows into Snowflake, where reporting and analytics deliver earlier business-day insight. CoCo agents pull maintenance, safety, and market context from the same foundation and return recommendations or SQL fixes through CoWork-enabled workflows.

Solution architecture

3 components · 3 layers
  1. Compute
    • Snowflake CoCo Powers AI agents and assistant workflows that combine operational context and recommend actions or query fixes.
  2. Storage
    • Snowflake Centralizes trading, pricing, risk, and operational data for business reporting and advanced analytics.
  3. Serving
    • Snowflake CoWork Supports interactive AI-assisted workflows used across power-plant and analytics use cases.

Architecture clues

  • A decade-old reporting platform was migrated into Snowflake as a core business system.
  • AI agents combine structured and unstructured operational context for maintenance recommendations.
  • CoCo is also used to diagnose non-performant SQL and encourage self-service optimization.

Evidence from the source

Before, huge data sets could bring us to a halt, but with Snowflake we haven’t had noticeable downtime.
Key information is already available at the start of the business hours
The AI agents have helped our colleagues gain reliable information and instructions for critical operational tasks
With Snowflake, Uniper now uses CoCo and Snowflake CoWork to drive new efficiencies across its power plants and analytics use cases.
we can now do AI at scale with CoCo
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
    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

    Uniper customer story

    First-party
    https://www.snowflake.com/en/customers/all-customers/case-study/uniper/
    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

  • Sitemap last modified Aug 7, 2026 · Snowflake · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Aug 7, 2026 · Snowflake · Unknown Legacy extracted date without source-page confirmation.
    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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