The research engine for buyer's agents and investors
A property intelligence platform built for Australian buyer's agents and investors. It replaces hours of manual suburb research with automated analysis across thousands of Australian suburbs, giving agents deep intelligence at a fraction of the time and cost.
In plain terms
The property research a buyer's agent used to spend hours on by hand, done automatically in seconds.
Research done for you
It reads the client's brief, pulls the data from a growing set of sources, and ranks the best-fit suburbs and properties in seconds instead of days.
Serve more clients
The slow part of every job runs on its own, so an agent's expertise reaches more people without the research bottleneck holding them back.
Answers you can defend
Every green, amber or red verdict shows the data behind it, so an agent can explain the recommendation to a client, not just quote a score.
The problem
Every client brief means hours of manual research across dozens of fragmented data sources: Domain for listings, ABS for demographics, council sites for zoning, SQM for vacancy rates, PropTrack for growth data. No single platform brought it together with intelligence layered on top.
Buyer's agents are capacity-constrained. The more thorough the research, the fewer clients they can serve. The platform needed to automate the research that slows agents down, so their expertise could reach more people, faster.
What we built
A full-stack subscription platform that aggregates a multi-source data pipeline, applies a multi-factor scoring model, and delivers suburb and property intelligence through a purpose-built interface for buyer's agents.
Suburb matching
An algorithm that ranks Australian suburbs across a wide range of signals (demographics, infrastructure pipelines, market cycles, rental yields, vacancy rates) by fit against each client's specific goals, budget, and risk profile. Results in seconds, not days.
Property scanner with traffic light verdicts
Paste any Domain or REA listing URL and receive a data-backed verdict from a multi-factor scoring model. Green, amber, or red, with the underlying data to explain the recommendation to clients.
13-section intelligence briefs
Comprehensive property and suburb analysis covering executive summary, financial projections, comparable sales, risk assessment, location intelligence, and a written analysis section. Exportable as white-label PDF reports for client delivery.
Portfolio tracking and wealth forecasting
Track owned properties with equity, LVR, rental yield, and net cash flow calculations. Scenario modelling projects wealth trajectories over 10, 20, or 30 years against investment goals.
36-question buyer profile engine
A structured buyer brief capturing personal situation, financial position, investment goals, risk appetite, and property preferences. The platform uses these inputs to personalise every suburb recommendation and property score.
A multi-source data pipeline
Government data (ABS Census, state valuer generals, crime statistics, bushfire/flood risk), commercial feeds (Domain API, SQM Research), and Google Maps enrichment for amenities and commute times, with further sources wired progressively as each is verified end to end.
Subscription and billing architecture
Three-tier Stripe billing (Essentials, Professional, Elite) with feature gating and usage-based property scans, wired to Stripe and ready for launch. Full auth flow with Supabase SSR and role-based access.
How it fits together
From client brief to client-ready report
Read it left to right: a buyer's brief goes in one end, the platform pulls and scores the data, and a finished, branded report comes out the other: the research an agent used to do by hand, run automatically.
Capture the buyer brief
A structured questionnaire records the client's goals, budget, and risk profile so every result is matched to them.
Pull the data sources
A growing set of government, market, and mapping feeds is aggregated into one place, the research an agent would otherwise gather site by site.
Match & score
Suburbs are ranked against the brief, and any listing is scored to a green / amber / red verdict.
Compile the intelligence brief
Findings are assembled into a multi-section report with projections, comparables, and a written analysis.
Deliver as a white-label PDF
The agent exports a branded report to hand straight to their client, and tracks the portfolio over time.
Every verdict shows the underlying data, so an agent can explain the recommendation to a client rather than just quote a score.
The outcome
The platform was built and deployed, but never went to market. The app ran behind its login, billing was wired to Stripe, and a scraping pipeline ran continuously in the background to deepen its national dataset. It turned hours of manual suburb research into seconds of data-driven intelligence, with multi-section reports an agent could deliver straight to a client. The build was delivered; the launch was the client's to make, and it did not happen. What was built remains the client's own research engine, not a subscription to someone else's tool.
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