Predict quality issues in custom part runs before they happen.
Small-scale manufacturers struggle with inconsistent quality control for custom parts when scaling from prototypes to production runs.
FabPredict analyzes past production data to forecast defect risks for upcoming runs. Input measurements from prototypes, get predictions on scaling issues like tolerances or material variances. Adjust processes proactively to hit consistent quality at low volumes.
Owners and engineers in small-scale manufacturing shops producing custom parts with low-volume runs
Machine learning predictions benchmarked against 1,000+ small-shop runs, with actionable process tweaks.
friendly
Import measurements from calipers/CNC exports.
ML model forecasts defect probability for new runs.
Test 'what-if' changes to parameters.
Track accuracy of past forecasts.
Threshold-based warnings for high-risk runs.
Compare your defect rates to industry anon data.
Auto-import from ERP tools.
Train on your shop's data.
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| params_json | text | No |
| actual_defects | int | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| run_id | uuid | No |
| predicted_risk | int | No |
| recommendations | text | Yes |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| run_id | uuid | No |
| dimension | text | No |
| value | int | No |
Relationships:
/api/runsUpload run data
/api/predictionsGenerate prediction
/api/runs/:id/outcomeLog actual defects
/api/dashboard/risksUpcoming predictions
Basic model only
1 shop
Teams
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 70 | 3% | $70 | $840 |
| Month 6 | 450 | 9% | $1,300 | $15,600 |
Data-driven forecasts for scaling custom production without surprises.
Target Reddit r/CNC and r/engineering with free prediction tool; scrape public CNC forums for emails and offer beta; partner with caliper app for cross-promo.
SPC stats
Overkill for small shops
Simple predictive SaaS for low-volume
Aggregated anon prediction data improves model accuracy over time.
Affordable ML APIs enable predictive QC; small shops adopting data tools post-supply chain disruptions.
Poor predictions without data
Seed with synthetic industry data
ML compute costs
Serverless optimization
Success: Model >80% accurate on test
Success: Users log outcomes, 75% accurate
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