Ekaterina Shalel

I study the hidden layer between being known and being chosen.

I opened my first business while I was still in medical school at Sechenov University. Four online stores, meant to cover the bills until I graduated. They outgrew that, and a few years later I sold them.

Then I went into cosmetology, close enough to products and client decisions to see how often what gets chosen is not the same as what is objectively best. Later I moved into technology and built AI products. The same problem kept reappearing in a different form: systems make decisions from incomplete evidence, compressed categories and imperfect context.

While architecting SKINBOT and later studying how general-purpose AI systems describe and recommend companies, I became interested in one distinction that now sits at the center of my work: recognition is not selection.

A model can know that a company exists, summarize it correctly and still leave it out when a user asks whom to hire, trust or buy from. That gap became my independent work on AI legibility.

Today I measure what AI systems retrieve, how they understand an entity, what evidence they can corroborate, which alternatives they select instead and what changes after a controlled intervention. The method is measure, diagnose, intervene and verify. Selection is observed, not guaranteed.

I also founded getmai.ai, an AI engineering company that designs and builds custom systems around real business operations. It began with unusually deep knowledge of appointment and repeat-visit workflows, but it is not limited to that category. The intellectual thread is the same: what happens in the hidden layer between signal, decision and action.

Medicine left me with a habit that matters here: separate observation from inference and never claim more than the evidence supports. A model rationale is not proof of its internal mechanism, and a before-and-after change is not automatically causal.