AI underwriting risk scores API – accelerate your insurtech MVP.
Solo indie hackers experience slow product iteration due to the lack of affordable APIs for real-time insurance quoting and underwriting.
RiskForge uses lightweight ML models on Supabase to score underwriting risk from basic applicant data like health/habits for life insurance. Developers get instant JSON scores to simulate carrier decisions. Perfect for rapid iteration without building ML from scratch.
Solo indie hackers and developers building insurance-related SaaS products
Pre-trained models optimized for indie scale + editable risk factors.
professional
POST applicant data for 0-100 risk score.
Test scores interactively.
Tweak weights via dashboard.
Score multiple applicants at once.
Audit past scores.
PDF risk breakdowns.
Email on high-risk scores.
User data fine-tune option.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| tier | text | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| key | text | No |
| calls_made | int | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| api_key_id | uuid | No |
| risk_score | int | No |
| factors_json | jsonb | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| weights_json | jsonb | No |
Relationships:
/api/scoreCompute risk score
/api/batch-scoreBatch process
/api/factorsUpdate custom weights
Basic model only
$0.0005/extra
None
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 40 | 4% | $24 | $288 |
| Month 6 | 250 | 7% | $262 | $3,147 |
Instant, customizable ML without the hassle.
Share ML model demo on Hacker News 'Show HN', recruit from insurtech Discord, offer lifetime Pro to first referrers.
Enterprise fraud detection
Overkill for MVPs
Indie pricing + simplicity
User-submitted data improves models via network effects.
Generative AI lowers ML barriers; insurtech funding shifts to bootstrapped indies.
Model accuracy issues
Rule-based fallback + docs
Success: 7/10 would pay
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