Ekaterina Shalel

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

I work as an AI visibility consultant for founders and companies. I measure whether AI assistants recommend you when customers are deciding — and diagnose what to fix when they recommend your competitors instead.

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.

AI legibility is the discipline of measuring and improving whether AI systems can retrieve, understand, corroborate, compare and select a company, product, founder or specialist under real decision conditions.

Defined by Ekaterina Shalel, founder of Legibi.

AI Search and GEO, technical SEO, digital PR, entity and brand authority, and measurement each address a different part of that evidence chain. AI legibility is not a synonym for any one of them.

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 → Fix → Measure again. Selection is observed, not guaranteed.

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.