Why 81% of Enterprise AI Initiatives Run Into the Same Wall
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The Hardest AI Problem in India is in a Field.
By 2050, India will need to produce 70% more food for 1.67 billion people – with less land, fewer farmers, and soil that’s been pushed past its limits for decades. The problem is real, the constraints are brutal, and the infrastructure behind it has to be built to match.
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End-to-End AI Inference: What It Takes to Run AI That Actually Scales
The shift from model deployment to production AI is messier than it looks. Here’s what an end-to-end inference platform actually does – and why the teams building it right aren’t thinking about models anymore.
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Shelf Intelligence: How AI is Rewriting Retail Execution
Most retail operations are short on speed, not cameras. The gap between what a camera captures and what a manager can act on is where stockouts linger, promotions fail silently, and quality issues compound. Can AI solve this? Read to find out.
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AI Infrastructure at Scale has a Visibility Problem
AI teams often struggle with understanding the reasons behind rising costs and inefficiencies in infrastructure due to disconnected monitoring tools. Unified visibility is essential for effective cost management.
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What We Get Wrong About Intelligence in AI
Perhaps the most important insight from this conversation is humility. Human intelligence itself is less about brilliance and more about adaptation. Culture accumulates heuristics. Communities coordinate under pressure. Systems evolve through constraints.
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The Economics of Intelligence: Why Smaller Models Win in Production
Voice AI, more than most AI applications, exposes the gap between what looks impressive and what actually works at scale.
This blog explores from our conversation with Akshat Mandloi – CTO & Co-Founder of Smallest.ai
