Multi-agent LLM web app that helps newcomers find the right Sydney suburb. Combines Reddit sentiment, crime data, transport scores, and OpenStreetMap into a conversational assistant. Won the Best AI LLM Project Award at UTS 2026.
An AI-powered suburb recommendation platform for Greater Sydney built as part of the Master of Data Science and Innovation at UTS. Combines structured government data (crime statistics, TfNSW transport), geospatial data via PostGIS, and community sentiment extracted from Reddit r/sydney using a DeBERTa and GoEmotions NLP pipeline. The system ranks all 656 Greater Sydney suburbs against a user-defined weight profile and answers natural-language questions through a multi-agent CrewAI pipeline with RAG and auditable evidence traces. I led the full backend architecture: FastAPI endpoints, the Synthesiser agent, in-memory liveability scoring, ArcGIS data ingestion, and the entire Next.js frontend including the onboarding flow, interactive map, evidence drawer, and report modal. The vector store runs on ChromaDB with MiniLM-L6-v2 embeddings and Supabase PostgreSQL as the structured data layer. Won the Best AI LLM Project Award in the UTS subject-wide Applied NLP competition (Autumn 2026), graded H with 98/100.
