Google Cloud Public Sector Singapore

National Library Board

NLB needed to deliver a highly personalized SG60 exhibition experience with real-time video, audio, and avatar interactions, but traditional cloud architectures could not satisfy the latency and data residency requirements for a sensitive public-sector deployment.

Deployed region’s first Google Distributed Cloud Connected for low-latency AI
almost every 10 minutes
orchestrated a complex ecosystem of 110+ agencies

Reported impact

Business outcome

NLB delivered the region's first Google Distributed Cloud Connected deployment for low-latency AI, created a seamless personalized SG60 visitor journey, and turned immersive storytelling into a secure public-sector AI showcase.

  • Compliance
  • Customer Experience
  • Scalability

Solution

National Library Board built the SG60 Heart & Soul experience on Google Cloud so each visitor could receive a personalized, emotionally responsive journey instead of a static exhibition. The core deployment uses Google Distributed Cloud Connected to bring AI compute close to visitors and process biometric inputs on site, which is essential for low-latency face-fusion rendering and strict data residency. Gemini models in Vertex AI power the digital concierge that converses naturally with guests and adapts the path ahead based on their aspirations, while Vertex AI also supports video generation and localized aging effects. The broader experience extends into the physical venue through audio guides using UHF and NFC triggers, and locally processed visitor contributions become part of a shared national roadmap. This architecture let NLB coordinate more than 110 agencies while keeping the technology invisible behind an immersive public experience.

Data flow

Visitors begin with a Gemini-powered conversation, their inputs and biometric data are processed locally on Google Distributed Cloud Connected, personalized videos and audio are generated through Vertex AI and face-fusion models, and their final aspirations are aggregated into a shared experience without sending sensitive data off site.

Solution architecture

4 components · 2 layers
  1. Compute
    • Google Distributed Cloud Connected Brings AI compute to the edge so personalized content can be generated with low latency and strong data residency controls.
    • Vertex AI Supports creative generation for personalized videos and immersive digital experiences.
    • Gemini models Drive the conversational and generative AI experiences inside the exhibition journey.
  2. Serving
    • Gemini in Vertex AI Powers the digital concierge that learns visitor hopes and tailors the experience in real time.

Architecture clues

  • Gemini in Vertex AI acts as a digital concierge at the beginning of the journey.
  • The design depends on edge AI compute so biometric processing can stay on site.
  • The experience combines physical triggers such as UHF and NFC with generative AI outputs.
  • Visitor aspirations are processed locally and added to a shared collective output.

Evidence from the source

By processing biometric data instantly on-site, the system renders high-fidelity videos of visitors’ future selves without the latency that breaks immersion
Deployed region’s first Google Distributed Cloud Connected for low-latency AI
Gemini in Vertex AI engaged guests in natural dialogue
With Accenture’s support, NLB orchestrated a complex ecosystem of 110+ agencies.
strict data residency requirements for a sensitive public sector project
Transparency Evidence & methodology 90/100 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 value90/100
  • 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 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

    National Library Board customer story

    First-party
    https://cloud.google.com/customers/nlb
    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 Jun 17, 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 Jun 3, 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.