I build AI systems that run in production, and I write here about what that actually teaches you.

Today I’m CEO and co-founder of Omnilogic Labs and of is4.ai. We design and run AI automation for banks and enterprises, along with the secure infrastructure underneath it. Some of it we host. Some of it runs on the client’s own servers with the source code handed over. I also work with Adaptig on AI training and education. Across all of it the job is the same: autonomous systems that have to keep working on a Tuesday afternoon when nobody is watching.

That’s the vantage point behind everything on this blog. I’m not observing AI from the outside. I’m accountable for systems other people depend on, which turns out to be a very different education.

Before AI was the whole job, it was the long game underneath a twenty-year career in enterprise software. I’ve built, shipped and sold software to Fortune 500 companies in financial services, healthcare, and government: enterprise application integration, CRM, data warehousing, business process work, and the strategy that decides whether any of it pays off. I’ve led teams across several countries, managed operational budgets above $5 million, and carried responsibility for sales over $50 million. The pattern running through all of it is that technology only matters when it moves a number the business actually cares about.

I started writing here in 2012. Innovation strategy, consumer trends, open innovation. In 2013 I argued that Bitcoin was a serious alternative to cash rather than a curiosity. In 2016 I wrote a post wondering out loud whether I should move to Silicon Valley. I did. In 2018 I wrote about AI’s coming effect on jobs. Then I mostly stopped writing, because I got busy building the thing I had been describing.

I’m writing again, from the inside this time.

What you’ll find here

• Field notes from production. Real stories from running AI systems, including the failures. The pipeline of mine that stopped publishing for 47 days without telling anyone is more instructive than any set of predictions.
• AI strategy for operators. Where AI earns its keep, where it doesn’t, and how to tell the difference before you’ve spent the money.
• Checked, not repeated. Most writing about AI tools is assembled from other writing about AI tools, which is how articles end up recommending products that shut down a year ago. When I write about tools, I verify the numbers myself and show you where they came from.

I write for the people doing the work: founders, operators, and the technically curious.

Find me

• Omnilogic Labs, AI systems that survive production: logic.fm
• is4.ai: is4.ai
• Adaptig, AI training and education: adaptig.ai
• LinkedIn: linkedin.com/in/powermeza
• GitHub: github.com/powermeza
• X: @powermeza

A note on how this gets made: I run my own content pipeline, the same kind of system I build for clients. It drafts, I edit and fact-check every line, and I sign it. Claiming expertise in production AI and then hiding that I use it would be a strange way to make the point.