Co-built with industry pioneer 
Cloth inspection table with AI vision
Current machine accuracy and it is improving
Manual inspection is no longer sustainable
Currently, garment factories check continuous rolls of fabric (thaan kapor) for defects at the start of production.
New defects occur when the fabric is cut into small pieces and processed further.
Human workers check cut pieces, which is slow, expensive, and has low accuracy.
With machine detection
Four times higher acceptance of consignment by buyers
Fewer faulty pieces ensure higher acceptance
Manual inspection has
low accuracy.
What it misses kills
your margin.
Missed detection sends defective panels to production
The burden grows with every line
Productivity
<2seconds
AI inspection time for each panel
4→1person
One operator replaces four inspectors
How Cloth Scanner works
AI + robotics engineering
Scroll down
Loading the 3D machine…
01Intake - panel arrives on bed
Panel arrives on the 1.8m x 2.4m working surface
Pen markPen markSpecification
Inspection<2sper panel
Sorting~900/hrat 1.2 m/s
Cameras2×4Kdual overhead
Gantry1.5m/sX and Y
Power2.4kWsingle-phase 220 V
GPU
The inspection
A closer look at fabric.
A clearer view of every defect.
Pen marksCharcoal panel
Dark textured fabric
Line markGraphite panel
Dark textured fabric
Surface marksIvory panel
Light solid fabric
Real-time dashboard
Every inspection.
Part of the bigger picture.
Cloth ScannerDashboardToday · 30 Sep 2026
Cut panels checked893,287 in the last 30 days
Defective panels found3154 in the last 30 days
Defective panel rate3.4%4.7% 30-day average
Most common defectHole42 of 172 defects (24%)
Daily inspection volume
1 – 30 SepPanels checkedDefectiveToday
Defects by type
172 defects · 30 daysHole42
Missing yarn35
Slub30
GSM Cut28
Pen mark19
Joint18
Found today
Hole · 2Slub · 1GSM Cut · 1
1 – 30 Sep 2026 · 154 defective panels
Found by AI model99.4%153 of 154 defective panels
Found by QC team35.7%55 of 154 defective panels
AI model advantage+63.7 pp98 more defective panels found
Caught only by AI9954 by both · 1 by QC only
Detection trend
Weekly · * part weekAI modelQC team
Detection by defect type
172 defectsDefectAI · QC
Hole97.6%35.7%
Missing yarn100.0%17.1%
Slub100.0%36.7%
GSM Cut100.0%50.0%
Pen mark100.0%47.4%
Joint100.0%50.0%
Sample inspection records
Insights
Reduce defects at source
01DEFECT TRACKING
Panel → defect → record
Records every defect with style, batch, and decision. Builds a traceable QA history.
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Data security
Trained on your fabric.
Kept in your factory.
Your factoryNo cloud. Never pooled into a shared dataset.
New defects go back into training
Your modelTuned for this factoryDefects it has learned
Hole
Slub
Missing yarn
GSM cut
Pen mark
Joint
HoleCloth ScannerRobotic unit with AI vision
- Ship to
- Your factory
- Delivery
- February 2027
No CapEx
Monthly fee for each unit with flexible options
- When we startInstalled at your factory
- Before it goes liveYour team trained, the model tuned to your garments
- While it runsUpdates and retraining included
- If a part breaksAddressed the same day