Shubham Nahar

AI needs Context

Contextex-Stiger Corpex-Appsmith

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Most companies think their AI problem is a model problem. It's not.

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It's a context problem. I see this every day. Enterprises deploying AI agents across finance, consulting, and operations. The teams that fail aren't using bad models. They're feeding those models without the institutional knowledge that makes the work actually work. The judgment calls, the edge cases, the how-we-do-things-here that lives in people's heads and nowhere else. And then there's the other thing nobody talks about loudly enough. Most of that knowledge is sensitive. Deal data, client financials, LP information. You can't pipe it through someone else's cloud and call it a day. The teams winning right now cracked two things: how to capture institutional context, and how to keep it inside their own environment. That's what I think about every day. I'm building at ai, helping enterprises go from "we should probably do something with AI" to agents running in production. Same models, better context, fully secure. We've seen teams go from 25% to 97% task accuracy without touching the model once. I write about what I'm seeing in the field. Where enterprise AI actually works, where it quietly fails, and why most deployments stall before they ever scale. If that's what you're working through, follow along.