Reliable Enterprise AI

Everyone is building AI products. Almost nobody is building reliable ones. The gap between capability and reliability is the defining engineering challenge of this moment — and most organizations don't even have a name for it yet.

Traditional search finds words. Semantic search finds meaning. The infrastructure shift behind that difference — vector databases — is becoming a foundational component of enterprise AI. Here's what architects need to understand.

We often think of AI as a technical breakthrough—algorithms, data, neural networks. But the real disruption isn’t in the code. It’s in how leaders learn to see AI not as a tool, but as a strategic partner.

Retrieval-Augmented Generation is the architectural pattern that makes AI systems actually useful in enterprise. Not because it makes models smarter — because it gives them something to work with.

The hype around large language models is real, loud, and often counterproductive. Here's what two decades of building software teaches you about separating what AI can actually do from what the demos suggest.

The hype around large language models is real, loud, and often counterproductive. Here's what two decades of building software teaches you about separating what AI can actually do from what the demos suggest.

The demo works. The production system doesn't. That gap isn't a model problem — it's an architecture problem. This is what enterprise teams consistently underestimate when they ship AI for the first time.

Before I choose a framework, evaluate a model, or design a pipeline, I'm asking questions that have nothing to do with capability. They have everything to do with what happens when the system is wrong. The teams that build reliable enterprise AI are not the ones that move fastest — they're the ones that answer these questions first.