The Architecture of a Modern Outsourcing Company - AI Was Supposed to Make This Easy, Right? [ukr]

What is really happening to outsourcing companies in the AI era? Clients have stopped asking, “How long will this take?” — they now come in with their own estimates. They have already reassessed what we should be capable of, without us making any promises or even agreeing with those expectations. Long-term planning has broken down, and with it, the financial model and the traditional role hierarchy.

This talk is about how we are navigating that shift, through two specific cases.

The first: a client asked for a 10× productivity increase starting with the very next sprint. We never promised that manual work would disappear — another vendor did. The client removed 6 out of 10 engineers from our team and replaced them with a “swarm of agents.” We’ll look at how that swarm was set up, why features still failed to make it to production, and why engineers are now being brought back.

The second: we made the same bet ourselves — pushing AI adoption as far as possible in an internal experiment. The result: 2.5 months instead of one, one out of four features rewritten after release, and hundreds of bugs.

But there were valuable lessons. We discovered that the real delivery bottleneck was pull requests, built a PR classifier around that insight, and defined an order of operations between Claude, the classifier, and developers — along with what we call the 80% rule.

And finally, we’ll address the question every outsourcing company now has to face: the same feature takes fewer billable hours at roughly the same rate. The client pays less, while we have less room to be wrong.