AWS Public Sector Singapore

GovTech

From streamlining administrative processes and reducing wait times to personalizing interactions, generative artificial intelligence (generative AI) has the potential to transform public service delivery with tailored, purpose-built solutions that better meet the community’s needs.

1 million
10 million
3 months
50 percent

Reported impact

Business outcome

GovTech plans to deepen its partnership with AWS to expand generative AI and AI/ML operations and best practices to more government agencies. By optimizing for cost efficiency, GovTech aims to facilitate fiscally responsible generative AI development among public sector organizations. “We're thrilled to partner with AWS to make generative AI more accessible and sustainable for our agencies.

  • Cost Reduction
  • Productivity Improvement

Solution

From streamlining administrative processes and reducing wait times to personalizing interactions, generative artificial intelligence (generative AI) has the potential to transform public service delivery with tailored, purpose-built solutions that better meet the community’s needs. To build and deploy MAESTRO, GovTech worked with AWS, including the AWS Generative AI Innovation Center, a program that pairs organizations with AWS science and strategy experts with deep experience in AI/ML and generative AI techniques. Public sector organizations can now use MAESTRO, to create cost-efficient generative AI tools tailored to agency-specific use cases. Within 9 months of launch, MAESTRO was adopted by 20 public sector organizations, corresponding to over 45 project teams and over 300 data scientists and ML engineers across Singapore.

Solution architecture

5 components · 2 layers
  1. Compute
    • Amazon Bedrock Hosts the foundation models and generative AI application logic.
    • Amazon SageMaker Supports model training, tuning, deployment, or managed ML inference.
  2. Serving
    • Amazon SageMaker Studio Gives data scientists a managed environment for experimentation and model development.
    • Amazon SageMaker JumpStart Provides reusable model starting points and templates for AI projects.
    • Amazon SageMaker Canvas Lets business users build or analyze ML workflows without deep coding.

Architecture clues

  • It developed a generative AI sensemaker tool that uses clustering algorithms and an LLM to extract insights from documents.

Evidence from the source

From streamlining administrative processes and reducing wait times to personalizing interactions, generative artificial intelligence (generative AI) has the potential to transform public service delivery with tailored, p
GovTech identified generative AI as a way to drive innovation, enhance public services, and improve lives within the community.
However, the high costs of operating LLMs at a government scale were key considerations for the organization.
Public sector organizations can now use MAESTRO, to create cost-efficient generative AI tools tailored to agency-specific use cases.
The Government Technology Agency (GovTech) is a statutory board of the Government of Singapore responsible for the delivery of digital services to the public.
To build and deploy MAESTRO, GovTech worked with AWS, including the AWS Generative AI Innovation Center, a program that pairs organizations with AWS science and strategy experts with deep experience in AI/ML and generati
Transparency Evidence & methodology 75/100 reference value · 85% 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 depth15/25
    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 scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence85%
  • 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 provenance8/10
    The publication date comes from publisher-controlled metadata.
  • 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. AWS

    GovTech customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/govtech
    Source type
    Vendor-published customer story
    Published
    Jan 1, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.