LLM Gateway vs MCP Gateway: Why Enterprise AI Needs Both

Modern enterprise AI operates across two separate channels that are routinely conflated: Reasoning: (Application → LLM): Where applications send prompts, receive responses, and consume inference. Execution: (Agent → Tool / Data): The path where autonomous agents query databases, call APIs, and modify enterprise state using the MCP. These paths have fundamentally different responsibilities and different […]

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IKEA AI case study: €13M Billie chatbot savings vs €1.3 billion design-service revenue

IKEA AI Case Study: How Billie Became a €1.3 Billion Business

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This IKEA AI case study begins with a chatbot named Billie and ends with a €1.3 billion business — and the distance between those two numbers is the most valuable lesson in enterprise AI today. Most companies deploy AI to do the same work for less. IKEA did that too: its Billie chatbot quietly saved

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AI Vendor Lock-In: Lessons From the Fable 5 Shutdown

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A frontier model went from “the most capable ever shipped” to completely offline in three days. For the enterprises that had already built on it, the disruption had nothing to do with model performance and everything to do with AI vendor lock-in — the underestimated risk of running a business on a single model you

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Build vs Buy AI in 2026: The Definitive Enterprise Framework

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When evaluating an enterprise technology stack, the build vs buy ai decision used to be a simple coin toss.Three years ago, most enterprises were building narrow prompt-response applications like basic chatbots, code copilots, or document summarizers. Your architectural choice usually came down to a straightforward question: do we call a frontier model API, or do

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Why AI Sycophancy Is a feature, not a bug — and why is it alarming

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OPINION | ENTERPRISE AI | THOUGHT LEADERSHIP By Sukrit Goel | Founder & CEO | April 2026 The next time ChatGPT tells you that your mediocre pitch deck is “really compelling” or that your half-baked product idea is “genuinely innovative,” know this: it is not malfunctioning. It is doing exactly what it was designed to

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Top 5 zero-shot object detection models in 2025

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Overview Zero-shot (open-vocabulary) object detection lets models find and localize objects they were not explicitly trained on — using language prompts instead of thousands of class-specific annotations. This changes how enterprises approach vision projects: faster prototyping, less labeling, and new opportunities for real-time automation. Top 5 zero-shot object detection models: 1. OWL-ViT What it is:

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