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Google Cloud Education Australia

UNSW Day of AI Australia

Customer Service Automation

They needed a consolidated infrastructure to manage costs and guarantee 100% safety compliance for thousands of students without manual oversight.

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Blocks 100% of harmful content using Shield Gemma
Day of AI Australia equips over 330,000 students with critical AI literacy through gamification using a secure, gen AI simulation.
Generates 120,000+ safe AI social posts for "Win the Farm" using open Gemma models
Scales safe AI simulations to support over 330,000 students to date with Compute Engine

Reported impact

Business outcome

Scales safe AI simulations to support over 330,000 students to date with Compute Engine; Blocks 100% of harmful content using Shield Gemma; Generates 120,000+ safe AI social posts for "Win the Farm" using open Gemma models; Deploys complex gen AI game in under four months with a lean team with Model Garden on Vertex AI

  • Compliance
  • Cost Reduction
  • Productivity Improvement
  • Risk Reduction

Solution

They needed a consolidated infrastructure to manage costs and guarantee 100% safety compliance for thousands of students without manual oversight. Supports 350+ concurrent student teams with auto-scaling from Vertex AI The Model Garden on Vertex AI lets us evaluate different models efficiently. We could just one-click deploy them and set up a testing environment. That made it so easy for us to test, which was critical because we only had three months, two engineers and a… Key components include (Open Models) supports a core part of the deployed AI and data workflow described in the customer story; Gemini on Vertex AI provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis; Vertex AI provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis; Cloud SQL stores transactional state and application data for the operational workflow at low latency; Compute Engine runs.

Solution architecture

6 components · 3 layers
  1. Compute
    • Gemini on Vertex AI Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Vertex AI Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
    • Compute Engine Runs serverless or virtualized application services with elastic scale for production AI workloads.
    • Gemma Provides the reasoning or generation layer used for classification, orchestration, content generation, or analysis.
  2. Storage
    • Cloud SQL Stores transactional state and application data for the operational workflow at low latency.
  3. Uncategorized
    • (Open Models) Supports a core part of the deployed AI and data workflow described in the customer story.

Architecture clues

  • An engineering team of two junior developers had only three months to build this secure simulation. Relying on fragmented tools from multiple providers for billing, inference, and security monitoring proved inefficient…
  • Continuing to leverage Google Cloud, Day of AI Australia and UNSW plan to launch an evergreen version of "Win the Farm" accessible year-round, as well as an ethics game as part of the 2026 program. The team is also…
  • Underpinning this simulation is a privacy-first data layer. Cloud SQL manages encrypted game states and teacher logs without ever storing student PII, ensuring strict compliance with Australian education standards. To…

Evidence from the source

Blocks 100% of harmful content using Shield Gemma
Day of AI Australia equips over 330,000 students with critical AI literacy through gamification using a secure, gen AI simulation.
Deploys complex gen AI game in under four months with a lean team with Model Garden on Vertex AI
Generates 120,000+ safe AI social posts for "Win the Farm" using open Gemma models
Scales safe AI simulations to support over 330,000 students to date with Compute Engine
Supports 350+ concurrent student teams with auto-scaling from Vertex AI
Transparency Evidence & methodology 95/100 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 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
    4 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
    6 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

    UNSW Day of AI Australia customer story

    First-party
    https://cloud.google.com/customers/unswdayofaiaustralia
    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 12, 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 27, 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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