AI predicts your next parts order to end stockouts and overbuys in auto shops.
Small auto repair shop owners face inefficient inventory management and poor parts cost tracking, causing frequent stockouts and costly overordering.
RepairReorderAI analyzes your repair history to forecast part needs and suggest optimal orders. It tracks usage patterns, costs, and lead times for automated reorder lists. Shop owners save time on guessing and reduce costs by ordering just-right quantities.
Owners of small auto repair shops
AI-driven predictions based on shop-specific repair data, not generic averages.
supportive
Track and categorize parts used per repair type.
Forecast demand and generate reorder lists weekly.
Suggest buy quantities based on cost per unit and trends.
Log supplier delivery times to refine predictions.
Visual charts of forecasted vs actual usage.
Test 'what-if' ordering scenarios.
Generate PO PDFs for suppliers.
Webhook for shop software sync.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| shop_id | uuid | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| name | text | No |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| shop_id | uuid | No |
| date | timestamp | No |
| vehicle_type | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| repair_id | uuid | No |
| part_name | text | No |
| quantity | int | No |
| cost | float | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| shop_id | uuid | No |
| part_name | text | No |
| predicted_qty | int | No |
| week_of | timestamp | No |
Relationships:
/api/repairsLog repair with parts used
/api/predictionsFetch latest AI predictions
/api/predictions/computeTrigger prediction recalculation
/api/usagesHistorical usage data
50 repairs/mo
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 80 | 3% | $60 | $720 |
| Month 6 | 700 | 6% | $1,050 | $12,600 |
Predict stockouts, optimize orders, and cut costs with repair-data AI built for auto shops.
Share MVP on Indie Hackers with free Pro for first 10. DM 15 shop owners from LinkedIn 'auto repair owner' search. Offer in Auto Repair Facebook groups with video demo.
Digital vehicle inspections
Weak predictive inventory
AI-first predictions at lower price
Multi-shop
No AI forecasts
Predictive edge with shop data moat
Proprietary repair-usage dataset for improving AI accuracy.
AI tools accessible via Vercel, plus volatile parts supply chains demand predictions.
AI prediction accuracy low initially
Start simple rules-based, iterate with data
Shops resist AI
Transparent 'explainable' predictions
Success: 70% willing to pay $25
Success: Accuracy >70%
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