EnterpriseSG · Aug 2022 – Feb 2025
Genie + Oracle
800+ non-technical users querying data via natural language. Agency's first LLM applications
Spot me teaching SQL! (LinkedIn) →800+
Active users
1,060
Prompts processed
36%
SQL query increase
4/5
User satisfaction
The Problem
EnterpriseSG is a Singapore government agency with 800+ employees. Institutional knowledge was locked in long reports, databases, and meeting minutes. Officers spent days searching for insights and there were no AI tools in the organization.
What I Built
Two complementary LLM applications:
Genie
Text-to-SQL tool enabling natural language database queries. Reduced SQL writing time from 15 minutes to 30 seconds.
Oracle
LLM chatbot for searching and summarizing meeting insights. Reduced search time from 3 days to 4 hours.
I also built an automated evaluation pipeline using LangChain to assess response quality, designed template/default prompt features (accounting for ~15% of all queries), and used KMeans clustering to analyze usage patterns and improve the product.
Shipping AI in Government
I created EnterpriseSG's first internal LLM policy, conducted a comprehensive risk assessment, and secured buy-in across 8 internal and external stakeholder platforms. I ran 10+ user testing sessions and sharing sessions reaching 300+ users, and gathered data from a 100+ participant survey to shape the roadmap.
Data-Driven Iteration
To diagnose declining usage, I used KMeans clustering to segment query types, identified that users were trying to ask quantitative questions Oracle wasn't designed for, and built a sentiment extraction feature that processed meeting minutes at scale to handle that need.