Insights

Everyone’s talking about AI models, but almost no one is talking about what those models run on.  You can’t just “plug in” AI to your current stack and expect it to hum. Whether you’re deploying off-the-shelf LLMs or building internal copilots, your existing infrastructure probably wasn’t designed for this kind of workload and that’s costing you more than you realize.  AI doesn’t run on hope. It runs on horsepower.  Let’s start with the obvious: AI is compute intensive. Even

AI vendors are everywhere. Every week, a new “copilot for X” hits the market. Each one promises to be smarter, faster, and more secure than the last. For startups and mid-market teams trying to move fast, it’s tempting to grab the coolest demo and run with it. But here’s the problem: AI vendor sprawl is real and it’s messy. What Starts as a Shortcut Turns Into a Minefield It begins innocent enough. Marketing grabs a GPT-powered content generator. Sales adopts an AI meeting as

Amidst all the AI excitement, there’s one silent performance killer that too many organizations overlook:  the network . If you’re investing in AI but haven’t looked at your connectivity infrastructure, you’re almost certainly leaving performance on the table. In fact, you might be breaking your stack without realizing it. Let’s get one thing straight:  AI is only as good as the network it runs on. You can have the best model, the cleanest training data, and a sleek, user-fri