AI-powered due diligence that reveals hidden short-term rental risks in strata buildings before you buy
Apartment buyers inherit costly, hard-to-fix problems from unregulated short-term letting when the body corporate lacks proper controls.
Prospective buyers upload body corporate documents, meeting minutes, and bylaws. Our RAG system analyzes them against known short-term letting red flags and public listing data to deliver an instant risk score, projected cost exposure, and plain-English report. Investors track buildings over time and receive alerts if new STR activity appears.
Prospective apartment buyers and investors in strata-titled buildings in short-term rental hotspots
The only tool combining document RAG analysis with real-time STR listing monitoring specifically for strata buyers — competitors are either generic home inspectors or host-focused AirDNA tools.
professional
Secure upload of PDFs, meeting minutes, and bylaws with automatic OCR and chunking
AI extracts STR rules, enforcement history, and risk indicators using embeddings
Generates 1-100 risk score with weighted factors and cost projections
Branded, downloadable reports with executive summary and supporting evidence
Track multiple buildings with historical risk trends
Periodic scans for new short-term rental listings in the building
Notifications when risk profile changes or new listings appear
Benchmark against similar buildings in the same suburb
Buyer's agents can brand and send reports to clients
Machine learning model predicting future special levies
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| role | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| address | text | No |
| suburb | text | No |
| risk_score | int | Yes |
| user_id | uuid | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| property_id | uuid | No |
| risk_score | int | No |
| findings | text | Yes |
| pdf_url | text | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| report_id | uuid | No |
| content | text | No |
| embedding | vector | Yes |
| created_at | timestamp | No |
Relationships:
/api/propertiesCreate new property and trigger analysis
/api/uploadUpload and chunk document for RAG
/api/analyzeTrigger OpenAI RAG analysis
/api/reports/[id]Retrieve report with findings
/api/alertsFetch user alerts and notifications
/api/subscribeCreate Stripe subscription
Limited to 1 building
Up to 10 buildings
Unlimited buildings
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 180 | 12% | $540 | $6,480 |
| Month 6 | 1,450 | 19% | $6,885 | $82,620 |
Upload your strata docs and get an AI-powered risk report in minutes. Know exactly what you're buying.
Offer 50 free comprehensive reports to buyer's agents in Sydney and Melbourne via LinkedIn outreach in exchange for video testimonials and referrals. Post case studies in Australian Property Investors Facebook groups (targeting strata owners). Partner with 2-3 active real estate agencies in short-term rental hotspots like Byron Bay who can white-label the reports.
Excellent market data for hosts
Not built for buyers or strata documents
Buyer-first focus with document intelligence
Local knowledge
Unstructured, anecdotal, time-consuming
Structured, instant, AI-analyzed
Data moat — every uploaded document improves the embedding database and risk model. Network effects as more users contribute building intelligence.
Multiple Australian states have introduced or tightened STR regulations in 2023-2024, dramatically increasing financial risk for buyers who inherit poorly governed buildings.
Liability if report misses major issue leading to buyer loss
Prominent disclaimers, insurance, and 'for information only' positioning
AI hallucinations in document analysis
Human review option for paid tier + rigorous prompt engineering
Buyers unwilling to pay $25/mo
Strong free tier with clear upgrade triggers during purchase process
Success: At least 18 confirm they would pay for this report
Success: 30% of respondents say they are 'very likely' to purchase
Success: Achieve $750 MRR and 4.5+ NPS
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