
About us
The STACK Community is the growing tech community by GovTech to gather everyone who shares this same excitement about using technology to improve the lives of everyday Singaporeans. Our aim is to collectively build a Smart Nation on the Singapore Government Tech Stack.
Join our community of members from across the public sector and industry now! Be a part of the STACK Meetup community and STACK Telegram group to receive latest updates on STACK by GovTech events.
About GovTech
GovTech is a part of the Singapore Government’s Smart Nation and Digital Government Group.
We develop new and meaningful digital products for citizens and businesses to elevate our efforts at Tech for Public Good. We are appointed as the lead for digitalisation efforts in the public sector to build long-term tech capabilities.
Featured event
![STACK Meetup [Data]: From Data to Meaning – Building the Context Layer](https://secure.meetupstatic.com/photos/event/9/4/a/9/highres_536138057.jpeg)
STACK Meetup [Data]: From Data to Meaning – Building the Context Layer
ANNOUNCEMENT – Registration via this link only.
GovTech (Punggol Digital District),
Tower 82, Level 8 @ Community 1 & 2,
82 Punggol Way, Singapore 829910
Join us at STACK Community's Telegram group (https://www.go.gov.sg/stacktelegram) to stay updated!
At our last STACK Meetup [Data] for 2026, we’ll tackle a challenge at the heart of data and AI: Context and shared meaning.
As data platforms mature and AI becomes more capable, making data available might no longer be enough. When definitions and business meaning differ across teams and systems, users can arrive at conflicting answers while AI can also produce responses that sound right but aren’t.
In this Meetup, GovTech and AWS will explore how shared context can help humans and AI interpret data consistently.
GovTech's Data Practice will share about context layers in data frameworks and introduce our conceptual blueprint for giving public sector data a shared frame of interpretation. This makes data available, understood, and trusted.
Building on the need for shared meaning, AWS will discuss how semantic layers, knowledge graphs, and ontologies can operationalise governed context for agentic AI. They will showcase how the accuracy, consistency, and explainability of AI outputs can be improved further.
Close out the year with us and discover what it takes to make data truly meaningful.
Reserve your seat for STACK Meetup [Data] today!: go.gov.sg/stackmeetup-24sep2026-mc!
Who should attend:
Data engineers, data architects, governance practitioners, and data product managers building standards and context layers that make enterprise data interoperable, discoverable, and AI-ready.
Programme
6:30pm: Networking & Light Bites
7:00pm: Introduction
By STACK Community
7:05pm: Opening
By Yap Ghim Eng, Head of Data Practice, GovTech Singapore
7:10pm: The Context Conundrum: Giving Public Sector Data a Shared Key Signature
By Janice Ng, Staff Data Engineer, Data Practice, GovTech Singapore
7:40pm: Beyond RAG: Building a Semantic Layer for Trustworthy Agentic AI
By Charis Wong, Senior Solutions Architect, Amazon Web Services
8:10pm: Panel Discussion
8:30pm: End of STACK Meetup
Click here* to sign up!
*Registration will be accepted via GovEntry only.
Upcoming events
1
![STACK Meetup [Data]: From Data to Meaning – Building the Context Layer](https://secure.meetupstatic.com/photos/event/9/4/a/9/highres_536138057.jpeg)
STACK Meetup [Data]: From Data to Meaning – Building the Context Layer
Community Room 1 and 2, Level 8 @ GovTech Punggol Digital District, 82 Punggol Way, Singapore 829910, Singapore, SGANNOUNCEMENT – Registration via this link only.
GovTech (Punggol Digital District),
Tower 82, Level 8 @ Community 1 & 2,
82 Punggol Way, Singapore 829910Join us at STACK Community's Telegram group (https://www.go.gov.sg/stacktelegram) to stay updated!
At our last STACK Meetup [Data] for 2026, we’ll tackle a challenge at the heart of data and AI: Context and shared meaning.
As data platforms mature and AI becomes more capable, making data available might no longer be enough. When definitions and business meaning differ across teams and systems, users can arrive at conflicting answers while AI can also produce responses that sound right but aren’t.
In this Meetup, GovTech and AWS will explore how shared context can help humans and AI interpret data consistently.
GovTech's Data Practice will share about context layers in data frameworks and introduce our conceptual blueprint for giving public sector data a shared frame of interpretation. This makes data available, understood, and trusted.
Building on the need for shared meaning, AWS will discuss how semantic layers, knowledge graphs, and ontologies can operationalise governed context for agentic AI. They will showcase how the accuracy, consistency, and explainability of AI outputs can be improved further.
Close out the year with us and discover what it takes to make data truly meaningful.
Reserve your seat for STACK Meetup [Data] today!: go.gov.sg/stackmeetup-24sep2026-mc!
Who should attend:
Data engineers, data architects, governance practitioners, and data product managers building standards and context layers that make enterprise data interoperable, discoverable, and AI-ready.Programme
6:30pm: Networking & Light Bites7:00pm: Introduction
By STACK Community7:05pm: Opening
By Yap Ghim Eng, Head of Data Practice, GovTech Singapore7:10pm: The Context Conundrum: Giving Public Sector Data a Shared Key Signature
By Janice Ng, Staff Data Engineer, Data Practice, GovTech Singapore7:40pm: Beyond RAG: Building a Semantic Layer for Trustworthy Agentic AI
By Charis Wong, Senior Solutions Architect, Amazon Web Services8:10pm: Panel Discussion
8:30pm: End of STACK Meetup
Click here* to sign up!
*Registration will be accepted via GovEntry only.16 attendees
Past events
84
![STACK Meetup [Cybersecurity], (17 Sep 2026)](https://secure.meetupstatic.com/photos/event/9/3/0/f/highres_536137647.jpeg)
![STACK Meetup [Engineering]: Composing a Digital Service Using Platform Products](https://secure.meetupstatic.com/photos/event/8/2/a/c/highres_536073452.jpeg)