For ecommerce and DTC brands
Be the brand AI recommends when shoppers ask which product to buy.
When shoppers ask ChatGPT for the best product in your category, do they hear your brand or a competitor's? Most ecommerce teams have no idea. We run the work that puts you inside the answer.
§01 Why AI search is reshaping ecommerce
The shopping query is leaving the search box.
ChatGPT now serves more than 800 million weekly users. A growing share of them are using it for product research before they ever open a retailer site. The behaviour is the same as a Google query a decade ago, with one important change: the result is a single named recommendation, not ten ranked links.
For an ecommerce brand, that compresses the buying funnel into a single moment. Either your SKU is the named answer, or it is not in consideration. Page-two ranking does not exist on a chat surface. There is no second click. The shopper hears two or three brands, picks one, and goes to checkout.
The work for ecommerce is no longer ranking pages. It is engineering the source pool that AI reads from, getting your SKUs cited inside that pool, and keeping the attributes consistent across every surface the model trusts.
§02 What we do for ecommerce brands
Six lines of work, run weekly, owned by us.
Each block describes the actual work, not a tool we hand over. You receive named outcomes on a fixed cadence; we carry the production, the publication relationships, and the platform monitoring.
01
SKU-level visibility audit on the prompts buyers actually ask
We pull the prompts shoppers run in your category, map each one to the SKUs you sell, and audit how AI answers today. The output is a SKU-by-SKU report of where you appear, where a competitor wins, and where the answer pool has no clear recommendation yet.
02
AI shopping prompt curation, owned and maintained
Your category has a working set of 80 to 200 shopping prompts that decide the sale. We curate that list with you on day one and re-run it every week against ChatGPT, Claude, Gemini, Perplexity, and AI Overviews. You stop guessing which queries matter.
03
Product attribute accuracy across AI source pools
AI answers pull product attributes from your PDPs, retailer pages, marketplace listings, and review sites. When those disagree, AI picks one and runs with it. We reconcile the attributes that decide recommendation, and we keep them aligned across surfaces.
04
Retailer-page and marketplace citation work
ChatGPT cites Amazon, Walmart, Sephora, and category aggregators more than it cites your own DTC site. We work the citation patterns those pages follow, brief retail teams on the structures that get pulled, and place your SKUs inside the answer pool the way AI reads it.
05
AI product recommendation surfacing across categories
Recommendation rate is the metric that matters: not whether AI mentions you, but whether AI recommends you over a named competitor. We engineer the third-party validation, review density, and category framing that move recommendation rate week over week.
06
Weekly drift monitoring and course correction
Product catalogs change. Retailer prices change. Review counts change. AI answers drift with all of them. We monitor the curated prompt set every week and respond inside seven days when a source-pool shift hits a SKU you care about.
§03 The outcomes we commit to
Named results, written into the engagement letter.
We deliver results, not dashboards. The pilot pricing is built around it. You pay €500 per month for tools and APIs plus your direct media spend. We carry the team. At the end of the 90-day pilot, if we hit the goal we agreed on day one, the engagement converts on a €6,000 success fee and a €2,500 per month retainer thereafter. If we miss, you walk. No further obligation.
Citation share on a fixed shopping prompt set
We define 80 to 200 shopping prompts for your category on day one. The success metric is the lift in your citation share across those prompts on a named platform.
Recommendation rate against named competitors
The harder metric, and the one that moves revenue. We commit to a target recommendation rate against the two or three competitors that already win these queries.
Source-pool position on category aggregators
Most shopping answers cite the same five to ten domains in each category. We commit to inclusion in that domain set, measured by direct citation in the AI answer.
SKU-level inclusion in AI Overviews
For Google AI Overviews on commercial product queries, the deliverable is your SKU appearing inside the Overview by brand and product name.
§04 Who this is for
DTC brands and category retailers whose PDPs are not appearing in AI answers.
The typical brand we engage runs between five million and two hundred million in annual revenue, sells across DTC and at least one major retailer, and has a small in-house growth team that has been told to figure out AI visibility on top of its existing remit. The PDPs are well built for SEO. The AI search problem is not.
You usually come to us because one of three things is happening. Your category competitors are appearing in ChatGPT answers and you are not. Your AI Overview presence is declining on the commercial queries you used to win. Or your retailer team is asking what to put on Amazon and Walmart pages so that AI cites your SKU, and no one inside the building has a clear answer.
§05 How we work
One framework, applied weekly. The methodology is public.
The work runs on the CITE framework. We comprehend the prompt set, influence the source pool, track citation and recommendation movement on a weekly cadence, and evolve the program as platforms shift. The research underneath is published openly.
§06 FAQ
The questions ecommerce buyers ask before they engage.
How does ChatGPT decide which products to recommend?
Why don't my PDPs appear in AI shopping results?
Does Amazon or major retailer presence help or hurt?
How do AI Overviews change product discovery?
What can ecommerce do that traditional SEO cannot?
Ready to become the answer AI gives?
Book a 30-minute discovery call. We'll show you what AI says about your brand today. No pitch. Just data.