Ksenai helps companies see how AI systems describe their business, where the gaps are, and what should change first. The work combines data science, AI product thinking, business knowledge structure, and practical judgment.
Ksenai is led by Ksenia Zybkovets and combines 25+ years in data science and analytics with direct work on AI systems, agents, and answer workflows.
Ksenai was created to bring a measurement-based, product-minded approach to a modern business problem: how companies are understood, compared, and referenced by AI systems.
The work is grounded in ranking, signals, evaluation, and decision systems — applied to AI-generated answers and business visibility.
Ksenai is built on direct work with AI systems, agents, and answer workflows, so recommendations stay practical, testable, and implementation-aware.
The goal is to identify what should change first: service clarity, proof, categories, FAQs, source signals, or answer structure.
Ksenai uses AO Engine — a working system of AI agents developed in-house — to help structure questions, review AI-generated answers, and organize findings. The agents support the analysis. Strategy, priorities, and final judgment stay human.
Agents help organize brand, category, comparison, and buyer questions for AI visibility testing.
Agents help examine how AI systems describe the company, what they miss, and which competitors appear.
Findings are interpreted by the founder and turned into focused recommendations a business can act on.
A focused audit shows where your company is visible, what AI systems misunderstand, which competitors appear, and what should improve first.