Use Case Intelligence RSS Search use cases Search
Google Cloud Retail United States

Catalyst Brands

Marketing Personalization

Catalyst Brands needed to speed up product and creative cycles across a portfolio of retail brands, because traditional design, asset iteration, and analysis workflows were too slow and expensive for a market that moves at digital speed.

Read the original story Explore all use cases

Reported impact

Business outcome

Catalyst Brands reduced time to market, accelerated creative prototyping, enabled non-engineers to produce working code, and created a shared AI operating model that helps its retail teams respond faster to fashion demand.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

Catalyst Brands uses Gemini Enterprise as the AI foundation for product design, merchandising, analysis, and internal prototyping across brands including JCPenney, Aéropostale, Brooks Brothers, Lucky Brand, and Nautica. Teams use Gemini to brainstorm and rapidly prototype new concepts, while visual models such as Veo help transform still images and sketches into dynamic videos that can be iterated without organizing additional creative shoots. NotebookLM and Deep Research give business teams faster ways to analyze complex information and ground product ideas in real data, and code-generation tooling lowers the barrier for non-experts to create working software or UI concepts. This architecture turns AI into a shared workflow layer rather than a niche experiment, helping Catalyst move from idea to market faster while also building the internal confidence and culture needed for a broader retail AI transformation.

Data flow

Creative and merchandising teams start with product or campaign ideas, use Gemini and visual models to generate and iterate concepts, enrich those concepts with NotebookLM and Deep Research, and then move promising outputs into code or production workflows with coding agents and enterprise AI support.

Solution architecture

6 components · 2 layers
  1. Compute
    • Gemini Enterprise Provides the enterprise AI foundation used across creative, merchandising, and development workflows.
    • Gemini Supports brainstorming, prototyping, product design, and merchandising analysis.
    • Veo Turns still images and sketches into videos for faster creative iteration.
    • Deep Research Accelerates competitive and merchandising research tasks.
    • Coding Agents Help non-experts and developers turn ideas into production code more rapidly.
  2. Serving
    • NotebookLM Makes it easier for teams to analyze complex business information and anchor concepts in real context.

Architecture clues

  • Code-generation workflows are being pushed beyond engineering teams to non-expert users.
  • NotebookLM and other Google-made agents are used alongside Gemini Enterprise.
  • The broader roadmap includes moving from reactive to explanatory analytics.
  • The company is using visual models to iterate on product imagery without repeated shoots.

Evidence from the source

Empowering non-expert users to deliver production code
Faster time to market using Gemini for rapid prototyping
Simplified in-depth analysis with NotebookLM
Transforming static assets into dynamic videos with Veo
With Gemini Enterprise, we’re able to turn these product design cycles around rapidly
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. Google Cloud

    Catalyst Brands customer story

    First-party
    https://cloud.google.com/customers/catalyst-brands
    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 May 8, 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 May 7, 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.

Related

Similar use cases

See all like this
Google Cloud Modified · Oct 9, 2026 Central Retail Food Retail Thailand Replaced ~2,000 monthly manual Excel reports with a single source of truth on BigQuery through Looker dashboards Central Retail Food unifies its ecosystem on Google Cloud, moving from fragmented data to conversational… Addresses 50% to 60% of customer service queries with automated chatbots built on Conversational Agents Customer Service AutomationMarketing Personalization BigQuery · Gemini · Gemini platform Google Cloud Modified · Oct 9, 2026 ICA Retail Sweden Previously, however, ICA’s fragmented technical environment was holding it back. Data was siloed across legacy platforms and SQL server environments, creating high maintenance costs and preventing teams from sharing… ICA Gruppen is a leading Swedish retailer, with a portfolio covering grocery, pharmacy and banking. Serving… Data Lake / Data Warehouse ModernizationMarketing Personalization BigQuery · Bigtable · Gemini Google Cloud Modified · Oct 9, 2026 Purplle Retail India Purplle needed to support rapid 6x user growth, survive peak sale traffic, and automate search, CRM, pricing, and assortment decisions without increasing infrastructure costs or slowing down retail operations. 10x traffic spikes Marketing PersonalizationReal-time Analytics BigQuery · Cloud AutoML · Cloud Pub/Sub