KOTAN.ai joins the data underneath ad monetization. Performance, revenue, and audience signals are resolved onto one key, so pricing and packaging decisions read the same numbers.
Publishers rarely lack numbers. They lack a joined view of them while the decision is still open. That is why so much yield work ends up as instinct with a spreadsheet attached.
Ad server, SSP, analytics, and CRM each describe the same inventory differently. Joining them by hand happens monthly at best, rarely in time to change anything.
Dashboards describe last month. Floors, packages, and pacing need the coming days, at the granularity the auction clears at.
When a change moves revenue, the reasoning lives in someone's memory. Nothing is auditable, reversible, or worth anything to the next person.
The commitments the product is designed around, and the ones worth holding us to.
Built by people who have run yield and ad operations, and evaluated the way it was built: against a publisher's own data, not a demo dataset.
Pilots run observe-only, on a segment the publisher already knows well. The decision log gets checked against what a human would have done before anything writes.
One inventory group, one action class. Bounds widen when the reasoning reads right, not on a schedule.
Writes are native and reversible. Turn the platform off and the stack is one a team can run by hand.
Predictive revenue modelling, audience segmentation, and one joined data set under both.
/dynamic-floor-pricingReal-time, per-segment floors. Every change records why.
/dedupingRepeat ads and competitive clashes stopped before the pod is built.
The most useful first conversation is about one segment, or one decision that takes too long. Not a product tour.