Case Study: Geospatial AI Chatbot Agent for Singapore
Location‑aware AI assistant blending maps, policies and live data to answer public & internal queries in natural language.
The Problem
Siloed data and manual policy lookups slow down accurate location answers.
Fragmented Location Data
Maps, policy rules and live feeds live in separate silos.
Slow Manual Lookup
Staff spend minutes assembling answers for each query.
High Inquiry Volume
Public & internal teams ask repetitive location questions.
Complex Policy Context
Rules must be applied correctly to avoid misinformation.
Our Solution
Unified geospatial data and policy‑aware natural language interface.
Unified Data Layer
Ingests geospatial, policy and live data into one index.
Natural Language Query
Understands intent and location references in plain speech.
Policy‑Aware Answers
Applies relevant rules before generating responses.
Scalable Chat Interface
Serves concurrent users with low latency streaming.
Results
60% Fewer Support Tickets
Self‑serve adoption reduces repetitive inquiries.
120s → 5s Response Time
Location answers generated in seconds instead of minutes.
95% Intent Accuracy
High precision classification for location‑based queries.
4× Self‑Serve Usage
More users resolve queries without staff intervention.