Expert-labeled property match training data for supervised models
Solo founders waste months failing to build accurate property matching algorithms due to no access to clean, large-scale real estate datasets
LabelMatch delivers thousands of pre-labeled property pair examples with rich annotations explaining why two listings match or don't match. Solo founders can immediately train supervised models or fine-tune existing ones without building expensive labeling workflows or struggling with ambiguous ground truth. Data covers both residential and commercial properties across multiple markets.
Solo founders and indie developers building proptech matching tools
Rich explainable labels that identify which specific attributes drove the match decision, plus coverage of rare edge cases that self-collected datasets almost always miss.
supportive
Access training batches with JSON labels and confidence scores
Browse and filter labeled examples with explanations
One-click export to CSV, JSONL, and HuggingFace datasets
Upload your predictions and get accuracy reports
Request labels for your specific uncertain cases
See inter-annotator agreement metrics
Commission labels for your niche market
Fresh labeled pairs added every week
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| created_at | timestamp | No |
| tier | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| key_hash | text | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| property_a_id | text | No |
| property_b_id | text | No |
| label | text | No |
| confidence | int | No |
| explanation | text | Yes |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| name | text | No |
| status | text | No |
| created_at | timestamp | No |
Relationships:
/api/labels/batchRetrieve labeled training pairs with filters
/api/evaluateSubmit predictions for model evaluation
/api/projectsList user's custom labeling projects
/api/exportExport dataset in requested ML format
1k records per month
50,000 records per month
Custom volume
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 65 | 11% | $250 | $3,000 |
| Month 6 | 650 | 16% | $3,640 | $43,680 |
Stop guessing what counts as a match. Get thousands of expertly labeled property pairs with detailed explanations.
Share detailed benchmark results on how labeled data improved model performance in Reddit's r/MachineLearning and r/SaaS. Offer free Team access for 90 days to the first 12 founders who apply via a Typeform linked from Twitter threads. Partner with 2 proptech accelerators to offer dataset access to their cohorts.
High quality data labeling
Expensive and generic, not real estate specific
Pre-labeled real estate specific dataset with domain expertise baked in
Large labeling workforce
Slow and very expensive for startups
Instant access to ready-labeled data at fixed monthly price
Proprietary labeling ontology developed specifically for real estate matching creates defensibility. User-contributed edge cases further improve the dataset over time.
With the rise of small fine-tuned models and retrieval systems, high-quality labeled data has become the primary bottleneck for solo AI builders in proptech.
Founders prefer unsupervised or self-supervised approaches
Provide clear benchmarks showing superiority of supervised approaches using our data.
Maintaining label quality at scale
Implement rigorous quality control with multiple reviewers and gold standard sets.
Success: At least 10 indicate strong intent to purchase
Success: 8 users integrate into training pipelines
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