Insights on Factory Intelligence, Safety AI & Industrial Vision
Expert articles on Connected Factory Intelligence, PPE detection AI, CCTV analytics, and AI-powered fabric inspection — written for manufacturing leaders, EHS managers, and operations directors worldwide.
The five questions that separate vendors who scale from vendors who pilot-and-die. A founder's checklist written from inside 7 years of production deployments — with scoring rubric you can run on any short-list in 20 minutes.
The camera and the inference chip are collapsing into a single device. An engineering view on sensor + chip choices, the three reference architectures emerging, and why chip-agnostic software is the only thing that survives multiple silicon cycles.
Cisco's 2026 State of Industrial AI Report landed with a number that should bother every manufacturing leader. A founder's view of the deployment gap — what scaled actually means, why most pilots die, and what shifted in the chip landscape in mid-2026 that finally makes closing the gap realistic.
MES tells you what happened. ERP tells you what was billed. Neither tells you what is happening right now. A practical guide to the live decision layer that mid-size and enterprise manufacturers are adding in 2026 — without replacing MES.
An audit is a sample of a problem you need to measure. AI PPE detection on existing CCTV cameras turns compliance from a quarterly inspection into a continuous, auditable signal — mapped to OSHA, RIDDOR, WHS, ISO 45001, and GDPR.
Most mills measure defect cost at the reject pile. That understates it by four to six times. A five-layer breakdown of what fabric defects actually cost — with a worked calculation mill owners can apply to their own factory.
Data-backed analysis of the most common defects, geographic patterns, and shift-time trends from CountAI's global deployment — the first public view of fabric defect behaviour at fleet scale.
Every factory has CCTV. Almost none of them use it for more than after-the-fact incident review. A practical guide to turning existing camera infrastructure into a real-time operations signal.
Lycra miss is the most expensive defect in circular knitting — and the hardest to detect. A technical look at why core-spun yarn defeats standard vision, and what it takes to catch it at the machine.
A comprehensive introduction to knitting inspection — why it matters, the types of defects found in circular knitting, and how AI-powered systems are transforming fabric quality control in modern textile factories.
A deep dive into the most common defect types in circular knitting, their root causes, and how AI vision systems detect each defect type differently to prevent downstream quality issues.
A detailed comparison of traditional manual fabric inspection versus AI-powered automated systems — covering detection accuracy, speed, cost savings, and how to evaluate the ROI of switching.