Now with AI root cause analysis

AI-powered textile defect detection,
built for production lines.

Our vision AI learns your fabric quality standards, then flags defects with evidence and context. It already spots holes, stains, streaks, weft & warp defects, needle marks, broken stitches, pinched fabric and knot/slubs — and gets sharper every time your QC team reviews a result.

Try live demo →
9+
Defect classes & growing
92.8%
Detection accuracy
<2s
Per image
AI
Root cause analyst
Trusted by textile manufacturers worldwide
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KoruSer
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Hassan Textile
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KoruSer
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Hassan Textile
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Watch it detect

AI vision that sees what you'd miss

Seam's YOLOv8 model scans every image, identifying and localizing defects with precise bounding boxes and confidence scores.

Every detection includes the defect class, exact position on the fabric, and a confidence percentage — giving your QC team actionable evidence instead of vague alerts.

Live simulation · Loops every 7 seconds
See it in action

A dashboard your QC team will actually use

Live defect trends, multi-class distribution analysis, pass rate tracking, and a Claude-powered AI analyst — all in a dashboard you can drag, reorder, and theme to match how your floor actually works.

app.seam.ai/dashboard
Dashboard
Real-time quality control analytics
Live
Inspections
0
Defects
0
Confidence
0.0%
Pass rate
0.0%
14-day quality trend
Defect distribution
0TOTAL
Hole42
Stain28
Lines15
Horizontal9
Vertical6
AI Analyst

Ask your production data anything

The built-in AI Analyst is filtered to your specific production line. It reads every inspection, spots patterns you'd miss, and explains root causes in plain language.

  • Powered by Claude Sonnet 4.6
  • Analyses every inspection, not just a sample
  • Context filtered per production line
  • Chat history persists across sessions
  • Suggests specific maintenance actions
AI Analyst
Powered by Claude Sonnet 4.6
Analyzing Line 2
Seam AI
Hi! Ask me about defect patterns, trends, or root causes on this line.
Ask about defect patterns, trends…
Platform capabilities

Everything your QC team needs, in one platform

Single Image Detection

Upload any fabric image and get instant AI analysis with annotated bounding boxes, defect locations, and confidence scores — in under 2 seconds.

InstantBounding boxesConfidence %
9 defect classes live today — detection library growing with every correction your team submits
Hole
A physical puncture or gap in the fabric.
Stain
Discoloration or contamination on the fabric surface.
Streak
A linear surface irregularity running through the fabric.
Weft defect
A broken or missing pick running across the fabric (weft direction).
Warp defect
A broken or missing yarn running down the fabric (warp direction).
Needle mark
A recurring mark left by a worn or damaged needle — an early wear signature, not a one-off defect.
Broken stitch
A broken or skipped stitch in a seam or edge finish.
Pinched fabric
Fabric caught or pinched during processing, leaving a compression mark.
Knot / Slub
A yarn knot or thickened slub embedded in the weave.
Tear / Cut
An elongated rip or cut in the fabric, distinct from a round hole.
Training in progress
Hole
A physical puncture or gap in the fabric.
Stain
Discoloration or contamination on the fabric surface.
Streak
A linear surface irregularity running through the fabric.
Weft defect
A broken or missing pick running across the fabric (weft direction).
Warp defect
A broken or missing yarn running down the fabric (warp direction).
Needle mark
A recurring mark left by a worn or damaged needle — an early wear signature, not a one-off defect.
Broken stitch
A broken or skipped stitch in a seam or edge finish.
Pinched fabric
Fabric caught or pinched during processing, leaving a compression mark.
Knot / Slub
A yarn knot or thickened slub embedded in the weave.
Tear / Cut
An elongated rip or cut in the fabric, distinct from a round hole.
Training in progress
How it works

From upload to insight in seconds

1

Capture

Photograph your fabric with any camera or phone. Upload individual images via drag-and-drop, or send hundreds at once through batch processing.

Drag & dropBatch uploadAny format
2

Detect

The YOLOv8 model processes each image in under 2 seconds, classifying defects and returning precise bounding boxes with confidence scores.

YOLOv8Bounding boxesConfidence %
3

Analyse

Results flow into your dashboard in real time. Ask the AI Analyst about patterns, get root cause explanations, and export reports for your team.

Live dashboardAI AnalystPDF export
Built for production

Designed for real factory floors

14-day quality trend

See quality drift before it costs you

Every inspection feeds a rolling trend chart. Spot gradual deterioration in defect rates days before they escalate into expensive rework or client returns.

Claude Sonnet 4.6 powered

Ask your production data anything

The AI Analyst is filtered to your specific production line. Ask "why are hole defects spiking?" and get a specific, data-backed answer — not a generic response.

  • Analyses all inspections, not just a sample
  • Context filtered per production line
  • Chat history persists across sessions
  • Recommends specific maintenance actions
Multi-line management

Track every line independently — benchmark them against each other

Create separate production lines for each machine or factory floor. Each gets its own inspection history, analytics dashboard, batch records, and AI analyst with persistent chat — so you can identify which line needs attention and why.

Production lines
Per-line
AI analysis
Real-time
Data updates
5 tabs
Overview · Batches · Images · Inspections · AI
Evidence archive

Complete traceability for audits

Every inspection stores the original image, annotated result, defect count, and timestamp. Your full QC history is searchable, filterable, and exportable.

  • Original + annotated images stored
  • Filter by defect type or batch
  • PDF & Excel report export
YOLOv8 model

Production-grade detection accuracy

Trained on real textile imagery across 9 defect classes today, with a 10th (tear/cut) in active training. Returns precise bounding boxes with per-class confidence scores — and improves continuously from in-app QC corrections.

Detection accuracy92.8%
YOLOv8
Object detection
9
Defect classes
Roadmap

What's coming next

Seam is shipping fast. Here's what's on the roadmap — in active development, in design, or lined up for the next few quarters.

📬
SHIPPING SOON

Daily & shift PDF reports

Wake up to a supplier scorecard in your inbox. Email summaries with trend charts, triggered alerts, and defect breakdowns — auto-sent at your chosen time.

EmailPDFCron
01/11
Have something else you'd like to see?
Pricing

Pricing is coming soon

We're still dialing in tiers with our earliest partners. Join the waitlist and we'll lock in a founding-customer rate when plans launch — plus early access to everything on the roadmap.

Starter
Coming soon
Work in progress
  • Single & batch processing
  • All current defect classes
  • Live dashboard & charts
  • Image gallery & archive
  • Community support
Most popular
Pro
Coming soon
Work in progress
  • Everything in Starter
  • Multi-line management
  • AI Analyst (Claude Sonnet)
  • Supplier scorecards
  • Alerts via email & Slack
  • PDF & Excel export
Enterprise
Let's talk
Custom pricing
  • Everything in Pro
  • Custom model training
  • Multi-factory rollups
  • SSO & audit logs
  • SLA & dedicated support
  • On-prem / data residency
FAQ

Common questions

What defect types can Seam detect?
Seam currently detects 9 defect classes using YOLOv8: holes, stains, streaks, weft defects, warp defects, needle marks, broken stitches, pinched fabric, and knot/slubs — with tear/cut detection in active training. Each detection returns a bounding box, defect class, and confidence score. Our detection library keeps growing through the in-app feedback loop, and Enterprise customers can request custom classes specific to their products.
Does the AI get better over time?
Yes. Every result includes a quick "is this correct?" check. Confirmations take one click; corrections let your team draw the right box and pick the real defect class. Every correction is automatically converted into training data and queued for the model’s next retrain — so accuracy improves continuously based on your actual fabric, without any manual labeling work on your end.
How fast is the detection?
Single images are processed in under 2 seconds. Batch processing handles multiple images simultaneously with live progress tracking — upload hundreds of images at once and they process in parallel.
How does the AI Analyst work?
The AI Analyst is powered by Claude Sonnet 4.6. It has direct access to your inspection data, filtered to the specific production line you're viewing. Ask anything about defect patterns, root causes, or maintenance. Conversation history persists across sessions.
Can I manage multiple production lines?
Yes. Create as many production lines as you need. Each gets its own analytics dashboard, batch history, image gallery, inspections log, and AI Analyst context — letting you benchmark quality across lines.
How is my data stored?
All images and inspection data are stored securely with Supabase (Postgres + object storage). Data is encrypted in transit and at rest. You can delete individual images or entire batches at any time from the gallery.
Can I export reports?
Yes — the dashboard supports PDF and Excel export of your quality data, suitable for client handoffs, supplier audits, and internal quality reviews.

See Seam on your fabric

Tell us your fabric type and defect challenges. We'll show you exactly what Seam catches — on your own images.

Live demoDetection on your actual fabric samples
Custom modelTrained specifically for your defect types
Fast setupStart detecting in days, not months
We'll respond within 24 hours.