Answer engines
GEO and AEO for ecommerce brands. When a shopper asks ChatGPT, Perplexity, Gemini or Google AI Overviews what to buy, one brand gets named. We make sure it is yours, with before-and-after citation data.
Trusted by leading ecommerce brands
Across client prompt sets re-run every month.
3.4x
Median lift in AI citations, 6 months
120
Buying prompts tracked per client
4
Engines monitored monthly
6.2
Median domains cited per answer
Client results
RUGGED COMPUTER HARDWARE, SHOPIFY
$556K in spend turned into $4.9M in tracked revenue
We rebuilt the account around non-brand search, cut wasted spend on duplicate queries, and pushed budget into the campaigns that actually produced first-time buyers.
Blended ROAS
Conversions
Conv. value YoY
CAR LIFTS, SHOPIFY
$696K in spend turned into $14.6M in tracked revenue
We created campaigns around shopping, display, search and Pmax. Focused on branded and non-branded keywords.
BLENDED ROAS
CONVERSIONS
CONV. VALUE YOY
Home goods, $8M/yr
99.4% of link equity held through a full Shopify migration
Every URL with a session in the trailing 18 months got a one-to-one redirect. Traffic dipped 6% in week two and was back above baseline by week five.
Link equity kept
Organic sessions
Mobile LCP
How citation is won
Answer engines pull from a narrower source set than people assume. Retailer pages, review roundups, and brand pages that carry clean, quotable specification data.
We define 120 real buying prompts, from "best waterproof duffel for carry-on" to "is [your brand] worth it versus [competitor]", and record every brand and domain each engine names. That baseline is your scoreboard.
Product, Organization, FAQ and Review schema, consistent across your site, Shopify metafields, Google Merchant Center and third-party listings. Models drop sources that contradict themselves.
Spec tables, unit-consistent comparison blocks and plain answers a model can quote without hedging, placed both on your pages and in the review and roundup sources those engines already cite.
Connected: built for extraction
Fragmented: built for a human skim
Baseline, correct the inputs, place the sources, re-measure.
01
Weeks one and two build the 120-prompt set from your category language and run it across ChatGPT, Perplexity, Gemini and AI Overviews. You get a table of who is named today and where the answer came from.
02
Weeks three and four fix structured data, unify naming and attributes across your site and feeds, and remove the contradictions that get a source discounted.
03
Months two and three build the comparison tables, spec blocks and plain answers on your pages, then get accurate data into the review and roundup sources the engines lean on.
04
Every month the full prompt set runs again. The report shows citation share by engine, which prompts you gained, and which competitor lost the slot.
Where structured product data changed who the engines name.
