FabPredict

Predict quality issues in custom part runs before they happen.

Score: 5.6/10SOHard Build
Brand Colors

The Opportunity

Problem

Small-scale manufacturers struggle with inconsistent quality control for custom parts when scaling from prototypes to production runs.

Solution

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.

Target Audience

Owners and engineers in small-scale manufacturing shops producing custom parts with low-volume runs

Differentiator

Machine learning predictions benchmarked against 1,000+ small-shop runs, with actionable process tweaks.

Brand Voice

friendly

Features

Data Upload

must-have15h

Import measurements from calipers/CNC exports.

Risk Prediction

must-have25h

ML model forecasts defect probability for new runs.

Run Simulator

must-have20h

Test 'what-if' changes to parameters.

Prediction History

must-have18h

Track accuracy of past forecasts.

Alert Setup

must-have16h

Threshold-based warnings for high-risk runs.

Benchmarking

nice-to-have12h

Compare your defect rates to industry anon data.

CSV Integrations

nice-to-have15h

Auto-import from ERP tools.

Custom Models

nice-to-have20h

Train on your shop's data.

Total Build Time: 141 hours

Database Schema

users

ColumnTypeNullable
iduuidNo
emailtextNo

production_runs

ColumnTypeNullable
iduuidNo
user_iduuidNo
params_jsontextNo
actual_defectsintYes
created_attimestampNo

Relationships:

  • user_id references users(id)

predictions

ColumnTypeNullable
iduuidNo
run_iduuidNo
predicted_riskintNo
recommendationstextYes

Relationships:

  • run_id references production_runs(id)

measurements

ColumnTypeNullable
iduuidNo
run_iduuidNo
dimensiontextNo
valueintNo

Relationships:

  • run_id references production_runs(id)

API Endpoints

POST
/api/runs

Upload run data

🔒 Auth Required
POST
/api/predictions

Generate prediction

🔒 Auth Required
PUT
/api/runs/:id/outcome

Log actual defects

🔒 Auth Required
GET
/api/dashboard/risks

Upcoming predictions

🔒 Auth Required

Tech Stack

Frontend
Next.js 14 + Tailwind + shadcn/ui
Backend
Next.js API routes
Database
Supabase Postgres
Auth
Supabase Auth
Payments
Stripe
Hosting
Vercel
Additional Tools
Vercel AI or HuggingFace for ML predictions

Build Timeline

Week 1: Core data and auth

40h
  • User setup
  • Run uploads

Week 2: ML predictions

45h
  • Prediction engine
  • Basic viz

Week 3: Dashboard and sim

40h
  • History tracking
  • What-if sim

Week 4: Payments and UI

30h
  • Stripe
  • Full landing

Week 6: Polish

25h
  • Alerts
  • Benchmarks
Total Timeline: 5 weeks • 220 hours

Pricing Tiers

Free

$0/mo

Basic model only

  • 10 predictions/month

Pro

$32/mo

1 shop

  • Unlimited predictions
  • Run simulator
  • Alerts

Enterprise

$99/mo

Teams

  • All Pro + benchmarks
  • Custom models
  • API

Revenue Projections

MonthUsersConversionMRRARR
Month 1703%$70$840
Month 64509%$1,300$15,600

Unit Economics

$28
CAC
$450
LTV
3%
Churn
85%
Margin
LTV:CAC Ratio: 16.1xExcellent!

Landing Page Copy

Predict & Prevent Part Defects

Data-driven forecasts for scaling custom production without surprises.

Feature Highlights

Risk predictions from past data
Process tweak recommendations
What-if simulations
Accuracy tracking

Social Proof (Placeholders)

"'Avoided a bad batch – priceless.' – Tom, Fabricator"
"'Data finally makes sense.' – Anna, Engineer"

First Three Customers

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.

Launch Channels

Product Huntr/CNCr/engineeringHacker NewsIndie Hackers

SEO Keywords

predict manufacturing defectscustom parts quality predictionproduction run forecastingCNC defect risk analysissmall shop quality analytics

Competitive Analysis

InfinityQS

infinityqs.com
Enterprise $10k+
Strength

SPC stats

Weakness

Overkill for small shops

Our Advantage

Simple predictive SaaS for low-volume

🏰 Moat Strategy

Aggregated anon prediction data improves model accuracy over time.

⏰ Why Now?

Affordable ML APIs enable predictive QC; small shops adopting data tools post-supply chain disruptions.

Risks & Mitigation

technicalhigh severity

Poor predictions without data

Mitigation

Seed with synthetic industry data

financialmedium severity

ML compute costs

Mitigation

Serverless optimization

Validation Roadmap

pre-build10 days

Collect sample data from 10 shops

Success: Model >80% accurate on test

mvp35 days

Beta predictions

Success: Users log outcomes, 75% accurate

Pivot Options

  • General manufacturing analytics
  • Prototype optimization
  • Material selection predictor

Quick Stats

Build Time
220h
Target MRR (6 mo)
$1,300
Market Size
$600.0M
Features
8
Database Tables
4
API Endpoints
4