Pillar
Most buyer's-agent software now claims AI. This page says exactly which parts are AI, which parts deliberately are not, and where a human still has to approve the result.
AI in buyer's agent software is useful in four specific places: an in-app copilot that can look things up and take actions on your behalf, outreach that runs on autopilot, grounded research that cites its sources, and parsing off-market agent emails into structured property records. It is deliberately NOT used for match scoring, which stays a transparent, repeatable calculation so you can defend a recommendation to a client.
Nearly every platform in this category now lists AI as a feature. Almost none of them say which model, on which infrastructure, doing what, with what human oversight. That vagueness is the problem: "AI: yes" in a comparison table tells you nothing about whether the software will save you an hour or quietly put a wrong number in front of a client.
So here is the specific version for AgentHub 360. Four places AI earns its keep, one place we deliberately refuse to use it, and the approval step that sits in front of anything that writes.
Copilot runs on Claude via Amazon Bedrock, on Australian-resident inference profiles in the Sydney region. That is a deliberate choice: your client data and your buyers' identity records stay onshore, which matters for the Privacy Act and for AML/CTF record-keeping.
It routes across three model tiers depending on the job: a fast model for classification and simple commands, a workhorse model for the main reasoning loop, and an escalation tier for complex planning. You do not pick; it picks.
The part that separates a copilot from a chatbot is that it holds tools. It can look up a client, navigate you to the right screen, check your setup status, add a note to a client record, create a task, and seed a new agency's setup checklist.
Sourcing off-market stock means staying in front of selling agents, constantly, in every suburb your buyers care about. It is the highest-value and most tedious job in a buyer's agency, and it is the one most worth automating.
AgentHub 360 runs it end to end:
Hub AI answers property and suburb research questions inside your workflow: planning and zoning changes, density reform, school catchment shifts, heritage overlays against development potential, gentrification signals, yield strategy and market risk.
It uses live web grounding rather than a static training snapshot, and it returns the sources it drew from. That distinction matters more than it sounds. A model answering from memory about NSW planning reform will confidently tell you what was true whenever it was trained. A grounded model retrieves the current position and shows you where it came from, so you can verify it before advising a client.
Pre-built query cards cover the questions Australian buyer's agents actually ask, so you are not writing prompts from scratch.
Off-market stock arrives as prose in an inbox. A selling agent emails you three properties in a paragraph, with the addresses, prices and features in whatever format they felt like.
AI parses those emails into structured property records: address, price guide, beds, land, features. Each one is then scored against every active buyer brief and surfaced to the right agent. What used to be a retyping job becomes a review job.
Property match scoring in AgentHub 360 is not AI, and that is on purpose. It is a weighted calculation: your brief's criteria are scored against the property's attributes, with the weighting shifting toward whichever criteria the brief specifies most tightly, and soft penalties for near-misses rather than hard knockouts.
The reason is that a score has to be defensible. The same brief and the same property produce the same number every time, and you can explain to a client exactly why one property scored 82 and another 61. A language model asked to rate a property would give you a plausible number that could differ on a second run and that nobody could audit. For a recommendation a client is paying you to make, that is the wrong trade.
What AI does contribute here is the explanation. Once the score is calculated, AI writes the plain-English reason a property fits the brief, in language you can put in front of a buyer. If that ever fails, the system falls back to rule-generated reasons and the scoring is untouched, because the score never depended on it.
Every AI feature above is built so the consequential step is yours:
Worth being equally clear about the limits. AI does not value a property, and nothing in the platform outputs an automated valuation you could rely on. It does not make the buy or no-buy call. It does not replace a building and pest inspection, a title search or a contract review. It does not lodge anything with AUSTRAC on your behalf; the platform prepares AUSTRAC-ready reports and your agency lodges them through AUSTRAC Online, because your agency is the reporting entity, not us.
FAQs
Answer questions the way AI and buyers ask them.
Full side-by-side pricing and features for every buyer's agent CRM in Australia, including year-one cost for a solo agent: