Private AI Built for Australian Real Estate

This is one of the primary design considerations for a real estate LLM deployment. During onboarding, we identify which states and territories your agency operates in and train the model on the relevant Residential Tenancies Act, Retail Leases Act, and associated regulations for each jurisdiction. The model is then configured to ask for the relevant state when answering compliance queries if it is not already clear from context. For agencies operating primarily in one state, the model defaults to that jurisdiction without prompting.

Yes. The model can be trained on your property records, inspection history, owner instructions, maintenance logs, and lease history so that property managers can ask questions like "what are the special conditions on the lease at 47 Smith Street?" or "has the hot water system at this property had recent service?" without navigating multiple screens. The depth of property-level knowledge depends on what records are ingested during onboarding.

A correctly designed deployment reduces your Privacy Act exposure compared with using public AI tools. The primary risk with public platforms like ChatGPT is APP 8, which governs cross-border disclosure of personal information. When you paste tenant details, financial records, or identification documents into a public AI, you are making a disclosure to an overseas entity. A custom LLM deployed on Australian infrastructure eliminates this disclosure entirely, keeping all personal information within your agency environment subject to your existing privacy policy.

Yes. One of the primary use cases for real estate agencies is making existing documentation actually useful. Most agencies have years of leases, inspection reports, maintenance records, and market data that sits in filing systems nobody searches. A custom LLM makes all of this retrievable in plain language, dramatically increasing the value of your existing documentation without requiring a separate digitisation program. New documents added to your management system are automatically indexed as they are created.

The model is trained on your existing communications, templates, and style guides as part of the knowledge ingestion process. This means drafted communications reflect your agency tone rather than a generic AI voice. For agencies with strong brand voice requirements, we spend additional time on the drafting calibration phase to ensure outputs match your standard. Property managers can always adjust drafts, but the starting point should require minimal editing.

A residential property management deployment typically takes four to six weeks from assessment to live use. Larger deployments covering both sales and property management, or multi-office agencies with significant documentation libraries, may take six to eight weeks. The first week covers assessment and data mapping, weeks two and three cover ingestion and training, and weeks four through six cover testing and refinement with your property management team. You will typically have a working system for testing by week three.