Enterprises use multiple AI models, but costs and risks rise without control. Model routers route tasks to the right model, improving efficiency and governance.
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.
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
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.
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.
