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Egypt’s AI Pyramid: The 15 Companies at the Base
The 15 companies building Egypt's AI: the national model, Arabic LLMs, chip startups and fintech tools, and why the best keep moving to the Gulf.
India’s Investor Onboarding Drop-Off Problem, and What AI Changes
A large share of Indian investors quit onboarding before funding an account. Shyam Arora on where the process breaks, and what integrated AI changes.
Where Does a First-Time Founder Even Begin With an AI Stack
Most companies starting with AI in 2026 make the same four decisions in the wrong order. Here is the sequence that actually works, and the questions to ask every vendor before you sign anything.
The Hidden Cost of Choosing the Wrong AI Eval Stack
A CXO framework for deciding whether to build or buy your AI eval stack, with a current read on RAGAS, LangSmith, Braintrust, Langfuse, and the FREE-AI Framework.
How Model Routers Cut AI Costs by Up to 70% for Enterprises
Enterprises use multiple AI models, but costs and risks rise without control. Model routers route tasks to the right model, improving efficiency and governance.
Are Indian Enterprises Paying Full Price for a Half-Built AI Product?
Global AI product is marketed as multilingual. The research, the regulatory landscape, and the economics of how these models are built tell a more complicated story for Indian deployments.
How to Evaluate AI Vendor Claims: A Technical Guide for CTOs and AI Leaders
What an AI vendor mean when he say hallucination-free, enterprise-grade, and 95% accurate. How to evaluate AI vendor claims: A technical guide for CTOs and AI leaders
Why Every AI Chatbot Seems to Give the Same Advice? The Artificial Hivemind Effect, Explained
When different AI models, trained by different companies, keep giving you the same answer, that is not a coincidence. In this piece, Chetanya Puri, Senior Machine Learning Engineer at CluePoints, explains why researchers now have a name for it, a dataset to measure it, and evidence that the problem runs deeper than anyone's system prompt.
Prompts, RAG, or Fine-Tuning? The AI Stack Decision Most Teams Get Wrong
ksheshkumar Ajaykumar Shah, Founder & CEO, Cogniify.ai, writes on why most enterprise AI teams choose wrong between fine-tuning, prompt engineering, and RAG, and how understanding the right architecture for each can turn AI from an expensive experiment into a scalable business tool.
