Cerevyn builds conversational AI — carefully.
We started in 2017 with a straightforward question: why do most AI assistants feel so mechanical? Since then, Cerevyny has grown into a remote mentorship platform where clients learn to design, build, and refine AI systems that actually hold a conversation.
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How the work actually happens
Each engagement at Cerevyny is structured around long-term continuity — not a single workshop or a course you finish and forget. Mentors stay involved across months, adjusting the plan as your AI system evolves.
Mentorship that runs on real feedback loops
Every client works through a defined arc — from mapping conversation intent to testing dialogue flows against real user inputs. Mentors review transcripts, flag where the system breaks, and suggest specific prompt or architecture changes.
The process is remote-first by design. Sessions happen via structured async reviews and live calls, so geography is never a constraint for the depth of guidance.
Architecture reviews
Intent mapping, slot design, fallback handling — reviewed in detail each cycle.
Prompt iteration
Prompt versions are compared systematically — not by intuition but by structured output testing.
Edge case coverage
Mentors specifically stress-test unusual inputs — the cases that break most systems in production.
Conversational AI Architect
Oryna Savchuk
Oryna focuses on dialogue state management and multi-turn context retention. She has worked on systems ranging from customer support bots to internal knowledge retrieval assistants.
Read her interview
Mentorship Lead
Daria Kovalenko
Daria structures the long-term engagement plans for each client. Her background is in instructional design, which shapes how Cerevyny sequences learning across a full mentorship arc.
Read her interview