Olga Bialkovich
Founder · AI and marketing strategy
Olga founded the lead-generation product that now runs as U [in] AI, and she sets the direction of the lab: which parts of marketing are worth automating, and which stay with people. Her working claim is a practical one — a system built around the right profile and the right message takes up to 60% out of the cost of finding customers and partners.
Her background is performance marketing rather than machine learning: SEO, paid and programmatic campaigns, conversion work, and the analytics that decide whether any of it paid back — ROMI, cost per lead, cost per click. That is the lens the agents are configured through, which is why the output here is judged by booked meetings rather than by how well the copy reads.
Across earlier projects her own track record includes revenue growth of up to 40% for clients, website traffic up 794% and lead volume up 139%, and building the marketing teams that ran those campaigns.
Works on
- AI strategy for marketing and sales
- Digital marketing: SEO, SMM, CRO, programmatic and paid campaigns
- Performance analytics: ROMI, CPL, CPC
- Brand positioning and identity
- Building and leading marketing teams
![Olga Bialkovich, Founder · AI and marketing strategy at U [in] AI](/img/team/olga-bialkovich-2026-10.webp)
Founder · AI and marketing strategy
7 articles
Written by Olga Bialkovich
U [in] AI founder Olga Bialkovich publishes new article on AI agent consistency in Unite.AI
Another conversation around U [in] AI has made its way into the international AI media space.
Why most AI pilots in marketing never reach production
Pilots are built to impress a steering committee, not to survive a Monday. What the fifth that ship do differently.
Three agents, one lead flow: the math behind −62%
We rebuilt qualification for an agri-tech distributor. Every number, every hand-off, every mistake on the way.
The 30-day funnel: how a five-person team grew pipeline by 75%
A five-person sales team, a list nobody had time to work, and thirty days. What the system did, what the people kept, and the numbers at the end of it.
Prompt libraries rot. Build evaluation sets instead.
A prompt that worked last quarter is a liability this one. What to keep instead, and how to know when the output has drifted.
Tone of voice is a dataset, not a PDF
Brand books describe a voice. Models need examples of it. How we turn forty pages of guidelines into training data.
GDPR and generative AI: a checklist for marketing teams
What has to be true before an agent writes to a European prospect — consent, records, retention and the part most teams miss.
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