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Google Cloud Software / SaaS Singapore

Imagination AI

IT Operations Automation · Marketing Personalization · Real-time Analytics

Imagination AI , part of Imagination Holdings, builds a portfolio of next-generation consumer applications across health, social media, and knowledge management. The company focuses on embedding adaptive, context-aware AI throughout its products and creation workflows.

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Cuts spec run times to 25 mins at ~$0.27 avg cost with Gemini
Delivers 93% model quality at 5% cost using Gemini 2.5 Flash-Lite
Generates 58 UI screens in 1 hour using Gemini and Google Stitch

Reported impact

Business outcome

Cuts spec run times to 25 mins at ~$0.27 avg cost with Gemini; Generates 58 UI screens in 1 hour using Gemini and Google Stitch; Delivers 93% model quality at 5% cost using Gemini 2.5 Flash-Lite; Secures local user privacy using Cloud Run hybrid reasoning.

  • Cost Reduction
  • Productivity Improvement
  • Revenue Growth
  • Scalability

Solution

Its technical recommendations significantly reduced BigQuery query and warehousing costs while transferring critical data engineering capabilities to internal teams. By staging raw source files in Cloud Storage and deploying web applications with Firebase App Hosting , Imagination AI established a unified, cost-efficient infrastructure. Combined with Gemini, this resilient Google Cloud foundation ensures the company can continuously iterate, launch context-aware applications, and scale its long-term vision for AI innovation. To build a structured, repeatable development model, Imagination AI selected Google Cloud to build its internal AI Design Spec Coach. Working alongside Google Cloud partner Tridorian to align foundational architecture, the team harnessed Gemini Enterprise Agent Platform to output typed, schema-constrained artifacts rather than conversational chat.

Data flow

stored or unified in BigQuery, Cloud Storage, orchestrated via Gemini Enterprise Agent Platform, and surfaced through Gemini Enterprise, Gemini 2.5 Flash, Gemini, Looker, Cloud Run

Solution architecture

8 components · 3 layers
  1. Storage
    • BigQuery Stores and queries the analytics and AI-ready datasets that power the workload.
    • Cloud Storage Stores large files, lake data, or static assets that feed downstream analytics and applications.
  2. Serving
    • Gemini Enterprise Provides a secure enterprise environment for teams to use Gemini with governed data access.
    • Gemini 2.5 Flash Handles low-latency generative AI tasks and natural-language interactions for the application.
    • Gemini Powers natural-language reasoning, summarization, generation, or assistance inside the workflow.
    • Looker Provides the governed semantic layer, dashboards, and self-service analytics used by business teams.
    • Cloud Run Runs application services and proprietary code in a managed container environment.
  3. Orchestration
    • Gemini Enterprise Agent Platform Provides governed enterprise access to Gemini models, agents, and regional controls.

Architecture clues

  • Cuts spec run times to 25 mins at ~$0.27 avg cost with Gemini
  • Delivers 93% model quality at 5% cost using Gemini 2.5 Flash-Lite
  • Generates 58 UI screens in 1 hour using Gemini and Google Stitch
  • Imagination AI uses Google Cloud and Gemini to automate app design workflows and deploy cost-effective, context-aware products.
  • Reduces data warehousing costs in BigQuery
  • Secures local user privacy using Cloud Run hybrid reasoning

Evidence from the source

Cuts spec run times to 25 mins at ~$0.27 avg cost with Gemini
Delivers 93% model quality at 5% cost using Gemini 2.5 Flash-Lite
Generates 58 UI screens in 1 hour using Gemini and Google Stitch
Reduces data warehousing costs in BigQuery
Secures local user privacy using Cloud Run hybrid reasoning
Transparency Evidence & methodology 95/100 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 value95/100
  • 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 impact25/25
    3 metrics 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

    Imagination AI customer story

    First-party
    https://cloud.google.com/customers/imaginationai
    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 Sep 22, 2026 · Google Cloud · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    Open date source
  • Unverified extracted date Sep 22, 2026 · Google Cloud · 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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