We design and build software systems that our clients' businesses run on. So it's not the product, it's the system that allows them to run their business. Typically, it's less "QuickBooks" and more "Let QuickBooks talk to 10 different systems" and then custom functionality built on that.

It's glue, custom business workflows, and basic web CRUD stuff. We build almost everything on Rails unless there's a critical reason not to (e.g., maintaining an existing system versus building from scratch.)

With very few exceptions our team composition is one senior engineer paired to a business. So we get to avoid a large amount of SDLC busywork which is inter-team communication. This leaves more time for client<->engineer communication which has a host of additional benefits. We also build with a "North Star" methodology which keeps everyone, including the client, laser focused on the work at hand.

To answer your final question about how we're benefiting so much from AI, I think it's primarily that we're leaning into it for both implementation, testing, and review. I know it's a sin to let AI review AI, but... it works. I'm actively skeptical of it myself, but our error rate and rework rates don't lie.

And we've got clients in various stages of development and/or long-term support. It's not like we're just hammering a bunch of stuff out and then bouncing. Most of these are multi-year tightly-integrated projects with our clients and we don't see a lack of trust or frustration that you'd expect to see if you were shipping slop. Our Honeybadger errors typically stay at zero, our performance metrics are acceptable across the board, and most importantly our clients love the work we're doing.

I can't think of any other way to measure the quality of what we're doing. And by those metrics, AI has made us better, not worse.

I should write a blog post to outline more of this in detail.