Advertising inside AI assistants, and AI-bid networks like AppLovin, are pulling budget away from the classic search and social duopoly. We help you test them properly instead of guessing.
Two things are happening at once. Assistants are becoming an ad surface: sponsored placements inside AI answers, where the unit of competition is relevance to an intent rather than a keyword bid. And AI-bid networks such as AppLovin have shown that a strong machine-learning bidder on non-search inventory can deliver ecommerce performance that rivals paid social. Both are early, both are noisy, and both reward advertisers who build a clean measurement layer before they scale. That is the work we do.
In assistant advertising, the model decides what is relevant. If it cannot describe your brand, it cannot place you.
— Arise GEO, AI Ad Platforms
Every new ad surface is cheap exactly once. The advertisers who test early buy the learning, not just the clicks.
— Arise GEO, AI Ad Platforms
We prepare your brand for assistant ad surfaces the same way we prepare it for citation: clear entity identity, structured product data, and answer-shaped content the model can place you against.
Machine-learning bidders live or die on signal quality and creative volume. We fix the conversion feed first, then run structured creative testing at the cadence the algorithm needs.
Every emerging platform over-attributes at first. We run geo holdouts and blended profit reads so budget decisions survive contact with your P&L.
New inventory should absorb incremental budget, not cannibalize a channel that is still efficient. We manage the whole portfolio to one profit target.
We can promise this because our methodology already works. Hundreds of pages now sit in position #1 across our clients' catalogs. The risk shouldn't be on you — it's on us.
Assistant ad surfaces are rolling out unevenly and availability varies by market and account type. The preparation work is available to everyone right now: entity clarity, structured product data, and answer-shaped content are what any assistant surface selects against, and they also drive organic AI citation in the meantime.
It is worth a structured test if you have clean server-side conversion signal, a healthy product feed, and enough creative to feed an algorithm weekly. It is not worth testing if your conversion tracking is unreliable, because the bidder will optimise toward noise.
Enough to exit the learning phase, held for long enough to read. In practice that usually means a fixed test budget over 6–8 weeks with a geo holdout, rather than a small trickle that never leaves learning.
Holdouts and blended reads. We compare total contribution profit with and without the channel active in matched geographies rather than trusting platform-reported conversions, which overlap heavily with search and email.
Yes. The same entity graph, schema, and answer-shaped content that get you cited organically also make you legible to the systems deciding which advertiser fits a given intent. The two programs share most of their groundwork.
Three layers, in order. First, entity clarity: one canonical Organization with @id anchors, sameAs links to authoritative databases, and knowsAbout coverage of your real expertise. Second, extractable content: atomic answer blocks, comparison tables, transparent pricing and specs, and FAQ blocks on every commercial page. Third, corroboration: mentions in the roundups, directories, review platforms, and trade publications those engines retrieve. Measure it by probing a fixed set of buying questions monthly and tracking citation frequency per engine.
Citability. AI engines lift passages that answer one question completely in 40–80 words, in plain language, with a concrete number, definition, or list. Pages that bury the answer under narrative rarely get quoted. We rewrite key pages so every important question has a self-contained answer block, wrapped in schema that labels what it is.
Yes, because AI answers are built from indexed pages. Every major assistant retrieves from a crawled index, so pages that aren't crawlable, renderable, or structured never enter the candidate pool. Classic SEO is now the entry requirement for AI visibility, and the two programs share almost all of the same work.
Technical recovery and on-page work typically show movement in 30–60 days. AI citation lift on long-tail and comparison queries usually lands in 30–90 days. Category-level authority and head-term rankings are a 6–12 month curve. Every engagement ships a sequenced 90-day plan so early wins fund the long work.
You get a forensic audit, a prioritized 90-day execution plan with effort and impact scoring, and optional done-for-you implementation by our engineers and editors. Pricing is scoped per engagement based on catalog size, locale count, and whether you want execution included. Implement our 90-day plan and if you don't see results in 3–6 months, we refund you in full.
Audits scoped within 24 hours. Results within 90 days, or your money back.
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