Profet AI’s Post

In #manufacturing, quality issues remain one of the most costly inefficiencies. According to the Institute of Industrial and Systems Engineers, poor quality can cost organizations between 5% and 35% of annual revenue, depending on product complexity. For a company with a turnover of $50 million, this translates to up to $17.5 million in avoidable losses each year. AI-powered computer vision systems provide a proven and scalable solution to this problem. Unlike traditional inspection methods, visual #AI operates continuously, without fatigue or bias. These systems detect both surface-level and microscopic defects, learn from real-time production data, and adapt to new conditions using active learning algorithms, which drive consistent and measurable performance at scale. Furthermore, AI inspection solutions offer several advantages: ◾ They can be easily scaled across multiple production lines and locations. ◾ They are resilient to distractions and human error. ◾ They can adapt to new defect types as #data accumulates. For organizations focused on reducing risk, improving yield, and scaling operations with confidence, AI-driven quality control is not a futuristic concept. It’s a proven tool for protecting revenue and fostering long-term resilience. At Techstack Ltd, we build the engineering foundations that companies grow on, enabling confident AI adoption that delivers business-critical outcomes.

🌟 The future of industrial quality control is being shaped by the growing adoption of Edge AI – local processing directly on the production line – and it is transforming the way factories operate. At Rosepetal AI, our computer vision and AI software makes this possible: it not only detects defects in products, but does so in real time, without relying on distant servers or slow connections. The result? Lower latency, fewer undetected errors, and higher efficiency at every stage of the production process. Our system automatically analyzes images and patterns, constantly learning to adapt to new product types and materials, reducing the need for manual supervision and delivering precision that is hard to achieve with traditional methods. Imagine: - Production line where every product is instantly verified, - Real-time alerts for operators for any issues, - Seamless integration with automation systems so robots or mechanical arms can react immediately, - And detailed dashboards providing management with actionable insights for data-driven decisions. This type of innovation not only improves product quality, but also reduces waste, optimizes costs, and increases customer confidence in production. With the right software, it’s not just about “seeing better,” it’s about reacting faster and preventing problems before they affect the final product. 💡 At Rosepetal AI, we believe that the combination of Edge AI and computer vision is key to building smarter, more sustainable factories ready to meet the challenges of the future. How do you think having an “intelligent eye” on every production line would transform your operations? #ComputerVision #EdgeAI #QualityControl #Innovation #RosepetalAI #DigitalTransformation #Industry4_0

When a machine stops just executing — and starts understanding — what truly changes in production? #MachineLearning is a turning point for #IndustrialAutomation. Machines no longer just follow instructions — they learn from data to improve every production cycle. They analyze variations, correct errors, and adapt in real time to keep performance and quality high. The result? Consistent quality. Less waste. Predictive maintenance. Smarter decisions. In this video, Cesare Librici – our CEO – shares how artificial intelligence is transforming automation into a system that evolves on its own. How do you imagine the next evolution of AI in #Manufacturing?

💡 AI doesn’t lower COGS - processes, design-to-cost, and ownership do. Many manufacturers overestimate the near-term impact of AI on cost structures in machinery and plant engineering. The reality: 👉 Without a clear process frame, design-to-cost approaches, and real execution capability, AI pilots stay on slides. 👉 What’s missing is hands-on line experience - only then do initiatives turn into measurable EBITDA levers. Put differently: 🚀 Without line know-how, AI stays a pilot - with execution, it becomes an EBITDA lever. COGS optimization needs more than technology. 🔍 It needs structure, accountability, and operational experience. ➡️ What has been your experience combining AI initiatives with real, measurable cost impact? #COGS #Manufacturing #Machinery #PlantEngineering #EngineeringExcellence #Transformation #EBITDA #ensignadvisory

𝐀𝐫𝐞 𝐲𝐨𝐮 𝐬𝐭𝐢𝐥𝐥 𝐫𝐞𝐥𝐲𝐢𝐧𝐠 𝐨𝐧 𝐦𝐚𝐧𝐮𝐚𝐥 𝐢𝐧𝐬𝐩𝐞𝐜𝐭𝐢𝐨𝐧 𝐭𝐨 𝐜𝐚𝐭𝐜𝐡 𝐝𝐞𝐟𝐞𝐜𝐭𝐬 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐥𝐢𝐧𝐞? AI and machine vision are no longer experimental—they’re becoming the new standard for quality control in smart manufacturing. Here’s what we’re seeing on the ground: AI-powered vision systems now outperform human inspection in speed, consistency, and precision. They catch micro-defects, measure tolerances, and reduce rework—freeing up skilled operators for more valuable tasks. Custom software is key to making this work. Off-the-shelf tools often can’t handle the complexity of real-world production. Tailored platforms give manufacturers the visibility and agility to make fast, data-driven decisions. And in high-precision environments, smart vision systems adapt to real-world variables—lighting, movement, orientation—delivering reliable results where traditional systems fall short. AI and Machine Vision Are Redefining Quality Control in Smart Manufacturing. See this and other smart manufacturing insights at: https://lnkd.in/gzB7GU9q

Why destructive testing doesn’t add up anymore? At Eigen, we often talk about helping manufacturers move from “prove it by breaking” to “prove it with data.” A recent study in The International Journal of Production Research supports that shift with hard numbers. The researchers developed a cost model showing how non-destructive inspection technologies can be built directly into production economics, not as an expense but as an investment that pays back through reduced scrap, energy waste, and rework. By embedding inspection early in the process, manufacturers can prevent costly downstream defects instead of paying for them later. That’s exactly what we’re building toward with our thermal and AI inspection systems, making in-line quality control not just technically possible but financially smart. The study reinforces what we see every day on the factory floor: smarter inspection doesn’t just improve quality, it changes how quality is valued. Source : https://lnkd.in/gb9QZ6tR? Learn more at : https://eigen.io/ #Manufacturing #ZeroDefects #AI #NDT #SmartInspection #ThermalVision #Operations #Data #Thermal #IR #Solutions #Factory

“𝑾𝒆 𝒉𝒂𝒗𝒆 𝒕𝒐 𝒕𝒉𝒊𝒏𝒌 𝒐𝒇 𝑨𝑰 𝒂𝒔 𝒂𝒏 𝒆𝒙𝒐𝒔𝒌𝒆𝒍𝒆𝒕𝒐𝒏 𝒇𝒐𝒓 𝒐𝒖𝒓 𝒎𝒊𝒏𝒅. 𝑰𝒕 𝒉𝒆𝒍𝒑𝒔 𝒖𝒔 𝒎𝒂𝒌𝒆 𝒅𝒆𝒄𝒊𝒔𝒊𝒐𝒏𝒔 𝒇𝒂𝒔𝒕𝒆𝒓, 𝒄𝒉𝒂𝒏𝒈𝒆 𝒐𝒖𝒕𝒄𝒐𝒎𝒆𝒔, 𝒂𝒏𝒅 𝒑𝒂𝒔𝒔 𝒌𝒏𝒐𝒘𝒍𝒆𝒅𝒈𝒆 𝒕𝒐 𝒕𝒉𝒆 𝒏𝒆𝒙𝒕 𝒈𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒐𝒏 𝒎𝒐𝒓𝒆 𝒒𝒖𝒊𝒄𝒌𝒍𝒚.” A direct quote from a conversation I had on Friday afternoon with Jason Thorne. Most conversations I hear about AI in the Chemical industry are still at the “what if” stage. But this was different as this organisation is already seeing results: 🟣 A proof of concept that unlocked $4–5m in working capital opportunities in a week. 🟣 Linking planning, logistics, and freight data into a single supply chain control tower. 🟣 Setting a bold goal: by 2026, having AI agents supporting (and making!) real-time decisions. That’s the kind of real world examples I want to know about. As part the new initiative I’m working on (that is still under wraps for now) I’m looking to feature companies and leaders in chemicals who are genuinely implementing AI, across operations, supply chain, manufacturing, or commercial. If that’s you (or you know someone I should speak to), drop me a message. #AIinRealLife #SpecialityChemicals #TheRobotsAreComing #KelhamPartners

🤖 𝗔𝗜 𝗶𝗻 𝗔𝗰𝘁𝗶𝗼𝗻: 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗶𝗻 𝗦𝘁𝗲𝗲𝗹 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 ⚙️ Artificial Intelligence is reshaping steel production — ensuring precision, minimizing defects, and driving next-gen manufacturing excellence. Here's how predictive AI transforms quality control. 🔹 𝐑𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐝𝐞𝐟𝐞𝐜𝐭 𝐝𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐀𝐈 🔹 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝐟𝐨𝐫 𝐳𝐞𝐫𝐨 𝐝𝐨𝐰𝐧𝐭𝐢𝐦𝐞 🔹 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐞𝐧𝐬𝐮𝐫𝐢𝐧𝐠 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐬𝐭𝐞𝐞𝐥 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝗥𝗲𝗮𝗱 𝗺𝗼𝗿𝗲: https://lnkd.in/gfBthzXZ #AIinManufacturing #SteelIndustry #PredictiveAI #QualityControl #IndustrialAI #SmartManufacturing #SteelTechnology

Before AI, there must be accuracy. At FRIGATE, we manage 33 factories manually ,tracking every order’s process plan, QAP, and dispatch readiness through human audits. Each supplier’s cycle time, rejection pattern, and part history is updated weekly by our execution team. No automation yet as of now but every log, deviation, and improvement is documented and standardized. The output: a real-world baseline for every machining and fabrication process before digitization. AI comes next. But discipline comes first. But this is only the start. Will scale this 33 to 300 soon, that's were tech comes to play Watch the space for more updates