Leading AI Engineering in Government
Reflections on building reusable capabilities, empowering teams and staying relevant in a fast-moving field
How we build AI for public good
A technical blog for AI science and engineering
Strengthening Chatbot Safeguards with Red-Teaming Agents
How we used agentic AI to proactively test and strengthen chatbot safeguards for chat.gov.sg
Decision Models for Guardrails: Exploring Jev and Kev for Moderation
A closer look at the new paradigm of frontier models, and how we explored Jev and adapted its open-source interpretation - Kev, for Singapore-context moderation.
Behind VEILED Expressions: A Singapore-context benchmark for mental-health AI evaluation
We built VEILED-SH to evaluate indirect self-harm/suicide language in the Singapore context, shaped by local language, privacy safeguards and practitioner insight.
Building a Serverless GenAI Platform on AWS
Balancing real-time performance, model cost and structured multi-agent workflows
Building a Smart and Safe Whole-of-Government Chatbot for Singaporeans
How we build custom policy guardrails and balance safety with helpfulness for chat.gov.sg
Stewarding What Was Entrusted
No Forward-Deployed Team really starts from zero, every achievement begins with a foundation laid by someone before us. Looking back on my journey, I realise what I inherited matters just as much as what I've accomplished.
When Agent Skills Evolve: The Need for a Layer of Control
What we learnt from building Rhizomatic, a human-in-the-loop proof of concept for reviewing, versioning and rolling back changes to reusable agent skills.
Guardrails in the Wild: Closing the Retraining Loop for LionGuard 2
Operationalising MLOps with production feedback to continuously evaluate, retrain, and improve our guardrails.
What is a Mixture-of-Experts model, really?
An experiment reveals how different layers shape what a model understands, retrieves and ultimately generates.
One Year in Multimodal AI at GovTech
“I came here expecting to build models for people who already knew what they wanted. I discovered that the harder work is figuring out what should be built, who it is for, and whether it solves a real problem.”
ORCA Part 2: Under the Hood
How ORCA works under the hood: routing each request to the right agent, running generated code in an isolated sandbox, and turning repeat chat tasks into reusable workflows.