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Google Cloud Retail Sweden

RELEX Solutions

Real-time Analytics · Supply Chain Optimization

RELEX needed to help retailers plan promotions faster and more accurately by turning complex retail data and forecasting models into an interactive, AI-assisted optimization workflow instead of a slow manual planning exercise.

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4+ hours of campaign management cut to <2 minutes

Reported impact

Business outcome

RELEX compressed promotion planning from hours to minutes, improved the quality of retail recommendations, and created an interactive AI layer that helps retailers act on data faster.

  • Productivity Improvement
  • Revenue Growth
  • Speed to Market

Solution

RELEX built its promotion-optimization stack on Google Cloud by combining large-scale retail data processing with Vertex AI and Gemini-powered agents. Cloud Storage ingests transaction histories, inventory levels, and promotional calendars, while BigQuery serves as the central data platform and Dataflow handles specialized large-scale transformations. Forecasting models that had previously lived in a more manual environment were migrated to Vertex AI so the team could manage versioning, performance monitoring, and the broader MLOps lifecycle in one place. On top of that foundation, RELEX is building a Gemini-based agent orchestrated through Google Kubernetes Engine that lets retailers interact with campaign and demand data in natural language, effectively creating a pocket pricing consultant that can turn analytics into immediate action. The platform reduces campaign-management effort from hours to minutes while improving the recommendation engine that drives retail promotions.

Data flow

Retail transaction, inventory, and promotion data lands in Cloud Storage and BigQuery, where Dataflow and forecasting models on Vertex AI process it. Gemini agents on GKE then turn those insights into natural-language planning guidance and promotional actions for retailers.

Solution architecture

6 components · 3 layers
  1. Compute
    • Dataflow Runs specialized, large-scale processing jobs over retail datasets.
    • Vertex AI Manages the forecasting-model lifecycle, monitoring, and MLOps operations.
    • Gemini Powers the natural-language promotion-planning agent layer.
  2. Storage
    • Cloud Storage Ingests and stores the large retail datasets used for promotion and forecasting workflows.
    • BigQuery Acts as the central platform for large-scale retail data transformation and analysis.
  3. Orchestration
    • Google Kubernetes Engine Orchestrates the AI agent that turns retail analytics into interactive planning workflows.

Architecture clues

  • Cloud Storage, BigQuery, and Dataflow form the retail data foundation
  • Forecasting models were migrated to Vertex AI for MLOps
  • The Gemini-based agent is orchestrated through GKE

Evidence from the source

4+ hours of campaign management cut to <2 minutes with AI Agent
Now, RELEX is enabling retailers to turn these high-level insights into immediate action with an agentic layer built using Gemini models on Vertex AI, and orchestrated through Google Kubernetes Engine (GKE).
Once the data is processed, it moves into the ML layer.
With Google Cloud, RELEX turns raw retail data into actionable insights.
the company recently migrated its forecasting models ... to Vertex AI
Transparency Evidence & methodology 85/100 reference value · 79% 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 value85/100
  • 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 specificity20/20
    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 confidence79%
  • 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 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. Google Cloud

    RELEX Solutions customer story

    First-party
    https://cloud.google.com/customers/relex
    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 Feb 21, 2026 · 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 Feb 20, 2026 · Google Cloud · 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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