Agents were knocking on the wrong doors. We built an AI engine that scores every property's likelihood to sell — then turns that signal into a proposal an agent can send the same afternoon.
Australian agents spend their week prospecting streets on gut feel — letterbox drops, cold calls, and guesswork. Most of that effort lands on owners who have no intention of selling, while the homes that are about to hit the market get missed by the agent down the road who knocked first.
We built a prediction model that scores any address by its likelihood to sell, wrapped it in a fast Android app, and surfaced it on a live suburb map and a ranked leads list — with the selling signals and insights an agent needs to act — so a signal becomes a listing without leaving the app.
A prediction engine and an agent workflow that turns a 'maybe' on a map into a signed listing.
A model that blends property history, market movement, and neighbourhood signals into a single Selling Score — and an honest timeframe for when the home is likely to list.
A live map that scores every property in a suburb at a glance, so an agent can see where the listings are before walking the street — backed by a database refreshed every day.
Less guesswork, more doors that actually open.
4 mo
Idea to Play Store
From the first prediction-model prototype to a published Android app in the hands of working agents.
85%
Top-score sell-through
3x
More listing appraisals booked
The Selling Score is trained on Australian sales history and market movement, so the prediction reflects the suburb an agent actually works — not a national average.
Founder
Founder, Get Listings
"TA Solutions turned a hunch I'd had for years into a working product. I stopped door-knocking blind — now I know which street to walk before I leave the office."
We worked hand-in-hand with the founder — an agent himself — pressure-testing every prediction against deals he'd actually closed, and shipping in weekly sprints with shared ownership of the model and the roadmap.
"They didn't just take a spec and disappear. They sat with me, watched how I prospect, and built the score around the way agents actually think."
Founder
Founder, Get Listings
"The first version of the model was good. By the third sprint it was scary-accurate — they kept tuning it against my real listings until I trusted it."
Founder
Product, Get Listings
Phase 2 moves Get Listings from predicting the listing to helping the agent win it.
Push high-scoring addresses straight into the agent's CRM with the proposal and contact already attached
Trigger personalised letters and SMS the moment a property crosses a score threshold
Agency-wide views that distribute the best opportunities across a team without double-ups
