Who we are.
We both spent years inside operating teams — carrying targets, running process, and doing the coordination work that fills a week without ever appearing on a roadmap.
We worked at the same company. Serhii ran revenue in tech consulting. Serge left to advise startups on where AI belongs in an operation and where it does not. We went separate ways and kept running into the same problem from opposite sides.
The problem was never getting AI to work. Prototypes worked. What kept failing was everything after: the integration that broke on a Tuesday, the model that changed underneath a prompt, the exception nobody had scoped, the permissions nobody had governed, the costs nobody was reading. The demo impressed people. Nine months later the workflow was being done by hand again.
Consultants were selling strategy. Agencies were selling builds. Platforms were selling tools and calling the operating problem the customer’s problem. Almost nobody was willing to stay and be accountable for the thing once it was live — which is the only part that decides whether it produces anything.
So that is what Common Sense does. We build custom AI coworkers, and then we run them.
Technology in service of outcomes.
AI is our leverage, not our reason for existing. We begin with what the business is trying to produce and what prevents it — and we have no attachment to a particular technology when a simpler one produces the outcome.
Decisions, not clicks.
A workflow is a chain of decisions, not a sequence of keystrokes. We design around what has to be decided, how much judgment each decision takes, and what happens if it is wrong.
Translation, not transformation.
We adapt AI to the operation you already run. No engagement should start by asking you to redesign the business around the software.
Useful autonomy, not maximum automation.
Automation percentage is not a success metric. The coworker acts alone where the evidence justifies it; everything else is drafted for approval or stays human. Responsibility allocated correctly beats humans removed.
Who leads the work.
Some processes run fully agentic. In others the coworker leads and people approve selected decisions. In others the expert leads, with agents expanding what they can research and prepare. We design for the right mode, not the most autonomous one.
Operated, not handed over.
We stay responsible for the coworker in production. You still own the decision map, the code, the prompts, and the recorded reasoning, and can take the whole thing in-house whenever you choose.
Being explicit about what we do not believe in is part of the method. If you hear any of these from us, hold us to it.
AI-first transformation.
Most companies do not need to redesign themselves around AI before getting meaningful value from it. Translation comes first; transformation, where justified, emerges from what we learn.
Automation-rate maximization.
“80% automated” is not inherently better than 40%. The meaningful question is whether responsibility is allocated correctly and whether the outcome improved.
Fully agentic as the destination.
Not every process should end up autonomous. For some work, human-in-the-loop is the permanent optimum; for others, human judgment is the primary source of value.
Demo-driven AI.
A convincing demo is not evidence that something should run inside a business. Production reliability, economics, observability, and failure handling matter more.
Headcount replacement as the default ROI story.
Occasionally it is the right economic argument. Usually throughput, consistency, speed, coverage, and capacity are worth more.
Technology-led discovery.
We do not open by asking where you want to use agents. We open with the outcome you want and the constraint preventing it.
If this is how you think an operating partner should work, bring us the workflow that is costing you the most. A first call is a conversation about one workflow and what could reasonably be delegated.