AI Marketing Agency

The system is the same; the buyer psychology is not. AI buyers evaluate before they commit — they watch a walkthrough, read a comparison, ask an answer engine, then sign up. So the weight shifts toward demonstration content, builder-educator creators and AI-search visibility, and away from the announcement-driven rhythm that works for a token launch. The KPI shifts too: signups and activation rather than reach or holder counts.

Signups and activation, tracked to source. We wire referral and attribution in before the first campaign runs, so every creator, placement and channel is judged on activated users rather than traffic or impressions. Weekly reports show what each channel produced against its target and what we are reallocating next. The attribution setup stays yours after the engagement ends, along with the creator performance data and channel benchmarks it produced.

Both. Pre-launch is often the better entry point: positioning, narrative, community groundwork and creator seeding done before launch mean you open to an audience that already exists. Attention also costs less before launch week than during it. For products already in market, the first phase is usually an audience and channel audit — finding where activation is leaking before adding more traffic to the top of the funnel.

That is what the AI-search visibility component works toward, and it runs inside the same engagement rather than as a separate purchase. We structure your entity and schema, publish an llms.txt, build authoritative citations, and monitor month over month how each major engine describes you so drift gets corrected. Nobody can guarantee a slot in a model's answer — anyone promising one is selling you something — but the inputs the models draw on are engineerable, and today the channel is far less contested than search.