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Arcade Alliance

Live demo · AI tool · Manufacturers with visual quality checks

Every part checked. Every shift.

A camera checks each part the same way at 3 am as at 11 am. Defects are boxed and named, the part is rejected, and the photo goes into a log your quality team can trust.

Try it live Make it yours

The problem

“Inspectors miss small defects at the end of a long shift.”
The problem this demo is built to solveLive demo · sample data

What is AI defect detection?

AI defect detection uses a camera on the production line and a computer-vision model trained on your parts to spot visual defects — scratches, dents, burrs, misprints — as each part passes. It signals a reject, saves the photo with the defect marked, and reports defect rates by shift and line.

Built around your workflow: this demo is a starting point — screens, fields, rules and integrations are made to fit your business.

Parts inspected

Before: By eye, when attention allowsWith the system: Every part, the same way

How the system works · how we measured

The demo checks each part with the same rules and sensitivity; a built system’s catch rate and false rejects are measured on your own parts before go-live.

01 · The problem

What problem does it solve?

A bracket manufacturer checks every part by eye at the end of the line. Inspectors are good, but a hairline scratch or a faint dent is easy to miss late in a 12-hour shift.

Defective parts reach the customer, the customer sends back a whole batch, and nobody can show which shift, machine or coil the problem came from.

  • Escaped defects

    Parts with defects reach the customer.

  • Batch returns

    One defect sends back a whole lot.

  • Tired inspection

    Attention drops late in the shift.

  • No evidence

    No photos to trace the cause.

02 · The solution

How does AI Defect Detection fix it?

  1. 01

    A camera that sees the same way

    An industrial camera with controlled lighting captures each part at the same angle, every time.

  2. 02

    A model trained on your parts

    The model learns from photos of your good and defective parts, labelled by your quality team.

  3. 03

    Sensitivity you set

    Each defect type has a confidence threshold — strict for scratches on visible faces, looser elsewhere.

  4. 04

    Reject and record

    A reject signal stops or diverts the part; the photo, defect and time are logged for the quality team.

03 · The result

What changes?

Parts inspected

Before: By eye, when attention allowsWith the system: Every part, the same way

How the system works · how we measured

The demo checks each part with the same rules and sensitivity; a built system’s catch rate and false rejects are measured on your own parts before go-live.

Parts in the demo
6
Defect types
4
Sensitivity levels
3
Saved for every reject
Photo

Live demo

Try it live.

A scripted demo with sample data, so it behaves the same for everyone. Nothing you do here is saved or sent anywhere.
  1. 1Press “Next part” to send parts past the camera.
  2. 2Switch sensitivity to High — the faint dent on part #1203 is now rejected.
  3. 3Turn on Auto and watch the inspection log fill.
Make it yours
Loading the demo…

Before & after

The same day, two ways.

Watch each task move from the way it’s done today to the way it runs on the system — or drag the slider yourself.
Today
With the system
  • Checking partsToday: By eye, getting tiredWith the system: Every part, the same way
  • A faint dentToday: Often missedWith the system: Caught at the sensitivity you set
  • Customer complaintToday: No evidenceWith the system: Photo log by shift and line
  • Inspector’s roleToday: Look at everythingWith the system: Review the doubtful ones

Make it yours

Built around your business.

Choose what your version of AI Defect Detection needs. Your brief builds itself — send it, and we reply with a plan.

Often asked for by businesses like yours

  • Defects that matter on each face
  • Line speed
  • Thresholds per customer
  • What happens to a rejected part
  • Who can change sensitivity
Always included
  • Camera & lighting set-up
  • Defect model
  • Reject signal
  • Photo log
Add modules
Connect to the tools you use
Runs on
Languages
Hosting
Team size

Under the hood

How is it built?

Where the data comes from, what the system does with it, and what comes out.

Inputs

  • Camera feed
  • Labelled photos of good and bad parts
  • Your quality rules

The engine

  • Computer-vision model
  • Thresholds per defect type
  • Reject logic

Outputs

  • Reject signal to the line
  • Defect photo log
  • Shift and line reports

Modules

  • Camera & lighting set-up
  • Defect model
  • Reject signal
  • Defect photo log
  • Sensitivity by defect
  • Shift reports

Connects to

  • PLC / line controls
  • Your ERP or MES
  • Quality management system
  • WhatsApp alerts
  • Network storage
  • Barcode readers

Stack

  • Industrial cameras & lighting
  • Python
  • Computer-vision models
  • Edge PC on the line
  • PLC reject signal
  • Next.js reports

Proof, the honest way

What would you measure?

This is a demo, so we don’t claim client results. In a real roll-out, these are the numbers we track from week one — before and after.

  • Escaped defects

    Defects found by the customer.

  • False rejects

    Good parts rejected by mistake.

  • Defect rate by shift

    Where and when problems start.

  • Customer returns

    Batches sent back.

FAQ

AI Defect Detection FAQ

All questions →
What is AI defect detection?

AI defect detection uses a camera on the production line and a computer-vision model trained on your parts to spot visual defects — scratches, dents, burrs, misprints — as each part passes. It signals a reject, saves the photo with the defect marked, and reports defect rates by shift and line.

How many photos are needed to train the model?

Usually a few hundred labelled photos of good parts and examples of each defect to start; accuracy improves as rejected parts are reviewed and added.

Can it keep up with our line speed?

Models run on an edge computer at the line and check a part in a fraction of a second; camera, lighting and trigger are designed for your speed.

What if it rejects good parts?

False rejects are measured from day one. Thresholds are tuned per defect, and doubtful parts can go to an inspector instead of being scrapped.

Does it need special lighting?

Controlled lighting matters more than the camera. We design an enclosure or lighting set-up so every photo looks the same.

Is the demo real computer vision?

No — the scene is drawn to show how detection, sensitivity and logging work. A built system runs a model trained on your parts.

Built around you

Every demo is a starting point.

Nothing here is sold as-is. Each one is rebuilt around your business — these are the things we tailor on every project.

  • Your workflow

    Steps, approvals and statuses the way your team works today.

  • Roles & permissions

    Who sees and changes what — by branch, team or person.

  • Fields & forms

    Your terms, your fields, your numbering and units.

  • Reports

    The numbers you check every day, in your format.

  • Integrations

    Tally, WhatsApp, payments, couriers and the tools you already use.

  • Your brand

    Your name, logo and colours on every screen and document.

  • Language

    English, Hindi or a regional language for staff and customers.

  • Hosting

    Our cloud, your cloud account or your own server.

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Updated 30 Oct 2026 · Arcade Alliance Network is a web, app, software and AI development company in Lucknow, India, building custom software for businesses in India and worldwide.

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