u/Monil009

I run a small studio called The Cubeless Company, building SaaS platforms, AI automations, and agents. I've been on the delivery side of this work for years, first leading a team at an IT company and now on my own.

Something I keep seeing, and it's the reason I'm writing this. Most AI projects don't fail at launch. They fail about three weeks later. The demo goes well, everyone's happy, then an edge case shows up that nobody wrote a fallback for and it fails quietly. Nobody notices for a month. Then the team goes back to doing it manually and decides AI doesn't work for their business.

The cause is almost never the model. It's that nobody asked what happens at 2am when an API returns something unexpected and there's no alert, no log and no owner.

So here's who we're wrong for.

Don't hire us if you need it by Friday. The reason projects break later is that nobody budgeted for the boring parts. Retries, error handling, monitoring, a place where failures actually show up. That work isn't glamorous and it's the difference between something that survives and something that doesn't.

Don't hire us if you want AI in it because it should have AI in it. Half the automation requests I get are better solved with ordinary code. It's cheaper, it's more reliable, and it doesn't cost you per token. I'll tell you when that's the case, even when it means a smaller project for me.

Don't hire us if you want everything in v1. Most first versions I see are three times bigger than they need to be. I'd rather ship the smallest thing that solves the actual problem and let real usage tell us what's next.

The whole reason Cubeless exists is software that keeps working after you log off. Not software that demos well.

Happy to answer anything about how this stuff actually breaks in production, or where I'd expect a specific build of yours to fall over. That part I'll do for free in the comments.