
AI isn’t the bottleneck. Infrastructure is. That’s the quiet killer of most enterprise AI efforts. You’ve got a cutting-edge agent prototype that performs well in demos, but it chokes the moment you try to move it beyond the lab. Why? Because real-world data isn’t clean. It isn’t centralized. It’s everywhere and traditional workflows demand you…

There’s a common misconception in enterprise AI:If your AI agent needs access to internal knowledge, you first need to build a vector database. It sounds logical until you actually try it. Suddenly you’re drowning in embeddings, data pipelines, security concerns, and months of effort just to get a prototype off the ground. But here’s the…
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