Ai for Business

  Moving Beyond the Draft: GenAI in Custom Manufacturing In ETO and MTO manufacturing, writing technical manuals is often a slow, manual data hunt. Engineers spend up to 30% of their time just chasing CAD models, ERP data, and supplier sheets. While some claim Generative AI can instantly "write" these manuals, the 2026 reality is much more practical. AI isn't replacing the engineer; it is acting as an automated assistant to handle the heavy lifting. ⚙️ The 70/30 Rule of AI Documentation The AI handles the 70%: It extracts data from CAD and ERP systems, builds the skeleton draft, populates part tables, and translates for global markets. The Human handles the 30%: Experienced engineers provide the critical "human-in-the-loop" expert review, adding the nuanced tribal knowledge that no software can replicate. 💡 For custom equipment manufacturers, the goal isn't to have completely AI-written manuals. The goal is to eliminate the "documentation tax" and...

In Made-to-Order (MTO) businesses, engineering teams rarely start from scratch — yet they often spend significant time recreating, modifying and searching for past designs 📐. Engineers must work across specifications, drawings, calculations, change requests and documentation while coordinating with multiple teams. Knowledge is usually scattered across files, folders and experienced individuals, making execution slower and harder to scale ⏳. AI can help by acting as an engineering copilot 🤖. It can assist in searching past designs, understanding specifications, generating documentation, analysing change impacts and helping teams reuse engineering knowledge more effectively. This can improve both speed and consistency while reducing repetitive effort 📊. In the next posts, the series will continue exploring how AI can transform other functions in MTO companies 🚀 #AIForMTO #AIInEngineering #Manufacturing #DigitalTransformation #GenerativeAI

Our CEO, Devesh Rajadhyax, recently delivered a session titled *“Beyond Search – How AI is Changing Knowledge Management”*, where he explored how Generative AI and Large Language Models are transforming the way organizations discover, manage, and interact with knowledge. The session explored the shift from traditional search-based systems to intelligent, AI-driven knowledge experiences — where users can interact conversationally with enterprise knowledge, uncover insights faster, and make better decisions across functions. As Devesh Rajadhyax noted during the talk, *“What ChatGPT did for individuals is what enterprises have been trying to achieve with Knowledge Management for decades.”* The session examined the limitations of conventional knowledge management approaches and the transformation being driven by the Generative AI era. To illustrate these changes, Devesh presented two contrasting examples — a large engineering enterprise and a small design firm.  At Cere Labs, building ...

We use apps like Zomato  and Swiggy  almost every day. Ordering food has become so easy 😌 that we rarely stop to think about what’s happening in the background ⚙️🤖 In this article, I’ve tried to break down where AI quietly works inside these apps—from what you see on the screen 📱 to how your food reaches you 🚚 It’s not very technical, just a simple way to look at something we all use 👍 If you get a few minutes ⏳, do have a read: 🔗 https://www.rajashreerajadhyax.com/post/ai-behind-the-scenes-in-swiggy---zomato If you find it useful, do share it with others as well 🤝✨ #AI  #EverydayAI #FoodDelivery  #Zomato #Swiggy #AIinBusiness #DigitalExperience