RentLens is an AI-native rental intelligence platform for the Australian market. Instead of leaving renters to piece together scattered information across listing sites, government portals, and maps, RentLens brings rent pricing, commute, safety, flood risk, noise, and community data into a single map-first interface — turning fragmented public data into a clear picture of what it's actually like to live at an address.
The platform is built around a core principle: AI as a data bridge, not a source of opinion. Rather than generating subjective verdicts, RentLens surfaces objective, sourced data and lets the user decide. It integrates large-scale open datasets — including ABS Census, government crime statistics, NBN broadband, flood and bushfire overlays, and a full three-state GTFS public transport pipeline that computes real per-line, per-direction departure frequencies — served from a self-hosted data layer to stay fast and cost-efficient at scale.
I built RentLens independently, end to end — from data pipelines and system architecture to UI and go-to-market. Its 1.0 launch reached 300+ signups in the first week, driven almost entirely by organic reach from a single LinkedIn post. It remains an ongoing exploration of how much friction you can remove between a person and a genuinely informed decision — and where the line sits between data that informs and data that decides.
- Next.js
- Supabase (Postgres)
- Google Maps Platform
- Overture Maps
- GTFS
- Gemini
