Pre-vectorized real estate datasets for instant property matching
Solo founders waste months failing to build accurate property matching algorithms due to no access to clean, large-scale real estate datasets
PropVector aggregates, cleans, and embeds millions of property records from public sources into a queryable vector database. Solo founders get immediate access via simple API calls for similarity search, eliminating months of data cleaning and embedding pipeline work. Multiple embedding models are provided so teams can experiment and select the best performing ones for their specific matching use case.
Solo founders and indie developers building proptech matching tools
Multi-modal embeddings (text, image, geospatial) specifically tuned for real estate with transparent scoring methodology and continuous model improvement based on usage patterns.
professional
Query properties by vector similarity with support for hybrid search
Full API access with official Python and JavaScript SDKs
Download filtered datasets in CSV, JSON, or Parquet
Web UI to browse properties and visualize embedding clusters
Real-time API usage, cost tracking, and popular queries
Test matching accuracy against verified pairs
Upload your own labels to improve base embeddings
Automated ingestion of new listings
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| created_at | timestamp | No |
| tier | text | No |
| onboarding_complete | bool | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| key_hash | text | No |
| name | text | Yes |
| created_at | timestamp | No |
| last_used_at | timestamp | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| description | text | Yes |
| record_count | int | No |
| embedding_model | text | No |
| updated_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| endpoint | text | No |
| calls | int | No |
| timestamp | timestamp | No |
Relationships:
/api/searchExecute vector similarity search with optional filters
/api/datasetsList all available datasets with metadata
/api/datasets/{id}/sampleGet sample records from a dataset
/api/keysCreate and manage API keys
/api/usageGet current billing and usage statistics
500 calls per month
20,000 calls per month
Usage-based overages after 200k calls
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 85 | 9% | $268 | $3,216 |
| Month 6 | 720 | 14% | $3,528 | $42,336 |
Skip months of data cleaning. Get production-ready vector embeddings from millions of real estate listings.
Post a detailed thread on Indie Hackers and Twitter about the pain of real estate data cleaning with before/after benchmarks. Offer lifetime 50% discount to first 8 founders who join from r/proptech and Product Hunt launch. Personally DM 20 solo founders building matching tools on Twitter offering free Pro access for video testimonials.
Massive traditional dataset
Not built for ML workflows or vectors
Purpose-built for matching with ready embeddings at indie-friendly pricing
Broad property attribute coverage
No embeddings or matching focus
Pre-computed vectors and benchmark suites specifically for algorithm developers
Continuously improved embeddings based on platform usage patterns and user feedback create a compounding data advantage that is difficult for competitors to replicate.
The surge in AI-powered real estate tools has created urgent demand for high-quality vector data while traditional data providers remain focused on enterprise sales teams.
Data sourcing and usage rights disputes
Exclusively use public records, county data, and synthetic augmentation. Engage real estate data attorney before launch.
Vector query costs at scale
Implement hybrid indexing, caching layers, and usage quotas from day one.
Developers prefer building their own data pipelines
Heavy emphasis on demos showing 10x speed to accurate matching.
Success: 75% indicate they would pay minimum $29/month
Success: 10 active users completing at least 50 searches each
Success: 150 signups and $1,200 MRR in first 30 days
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