Knit-I is the world’s most deployed AI knitting inspection system — 500+ systems across 7 countries. Detects fabric defects in 20 milliseconds and halts the machine automatically at full production speed.
Real Knit-I deployment detecting fabric defects at production speed
Detects defects and halts the machine in 20ms. No defective fabric produced beyond the detection point.
Eliminate manual inspection dependency. Reduce labor needs by 40% with centralized monitoring.
Run machines at higher RPM without fear. +1 RPM = +12 kg/machine/day.
Auto-generated fabric inspection reports. Grade every roll. Full defect history.
Real-time dashboards, ERP integration, management reports. Transform your knitting floor.
Each of these is defined in ASTM D3990. Our free knitting defects glossary gives the causes and remedies for every one, and the 4-Point System calculator grades a roll under ASTM D5430.
Custom-built camera detects sub-millimeter defects at 90 RPM. Multi-spectral illumination. Mounted 30cm from needle bed — 7× closer than competitors.
Powered by Intel® Core™ processors and listed on the Intel® Edge AI Partner Showcase as an official manufacturing inspection solution. 30–40 FPS on-device inference. No cloud, no latency.
Intuitive touchscreen interface. Real-time defect images with names. Minimal training required. Operators resolve defects promptly.
Knit-I retrofits into your existing circular knitting machines — regardless of make, model, or gauge. The most versatile knitting inspection system available.
All circular knitting machines — any diameter, any gauge
150+ supported
Cotton, polyester, elastane, blends, melange, dyed
0.5–1% fabric wastage eliminated.
Machines per operator: 2 → 4.
Eliminate manual inspection.
+1 RPM = +12 kg/day.
“Knit-I reduced defects by 95%.”
“Payback achieved within 6 months.”
“Our customers have started demanding Knit-I fabric, as they get 1–2% savings.”
“80% of our defects have been eliminated.”
“Captures all faults and gives a very precise report during the knitting itself.”
“Had we implemented a bit earlier we could have saved 4 tons of fabric in October 2024.”
Holes, needle lines, lycra (elastane) faults, oil spots, contamination, fabric breaks, weight variation, and the long tail of mill-specific defects we have trained on across 4,500 cameras and 7 countries. Detection happens at 30 cm of fabric — before the defect runs into hundreds of yards of waste — with sub-50ms inference latency at the machine.
Yes — and this is the category where Knit-I most strongly outperforms IR-sensor systems and cloud-based vision systems that fail on dyed lycra fabric. ROI on lycra and double-jersey machines is typically 3-4 months versus 6-8 months on simpler fabrics.
It is built for exactly those conditions. The system has been hardened across 6 countries including factories at 40°C with fabric dust in the air, on unstable power supplies, with no reliable internet. Edge-first architecture means no cloud dependency. Operators interact through a local-language touchscreen.
No. All AI inference runs on an on-premise edge compute box at the machine. Internet is optional for the cloud dashboard that plant managers use to see cross-machine reports — the per-machine detection and operator UI work fully offline.
Single Jersey, Double Jersey, Lycra blends, Mayer, Pailung, and most major circular knitting machine brands. Installation does not require modifying the machine — the system mounts at the take-down roll.
Under 24 hours for the first 5 machines. Operators are trained on the local-language UI on day one. Plant managers see the cross-machine cloud dashboard from week one. Buyer audits become easier from month one.
We install Knit-I on your machines — completely free for a trial period. If the results don’t speak for themselves, there’s nothing more to discuss.
Deployed today in India, Bangladesh, Sri Lanka, Uzbekistan, Peru, Indonesia and Turkey — we ship, install and support internationally. WhatsApp is usually the fastest way to reach our team.
Or reach us directly: [email protected] · +91 89259 32958 · WhatsApp