The specialist AEO and GEO agency, with our own AI search monitoring platform.
Focus vertical — E-commerce & Retail.

Generative engine optimisation, or GEO, is the work of getting your brand and products surfaced, described accurately and recommended inside answers from ChatGPT, Claude, Gemini and other AI tools. For e-commerce and retail, this matters because shoppers now ask AI which brand to buy before they ever open a marketplace or visit a brand site. By the time they reach a product page or a checkout, the consideration set is often already decided, and the brands inside that set are the ones AI search consistently surfaces with the right category, the right price tier and accurate product attributes.

Answer engine optimization (AEO) and SEO operate on different signals. SEO is about ranking pages on Google. AEO is about how language models describe your brand and products when a shopper asks for a recommendation. Google reads pages and links. Language models draw on training data, third-party citations, retailer listings, review platforms, editorial coverage and how clearly your category, price tier and attributes are described across the web. You can hold page one on Google for category terms and still be invisible in ChatGPT and Claude responses, and in many retail categories the brands that dominate AI search are not the ones that dominate Google.

The honest answer is that you cannot know from spot checks. Running a few prompts in ChatGPT tells you almost nothing, because language models return different answers across users, sessions and phrasings of the same query. To get a reliable read on category recommendation presence you need continuous monitoring across ChatGPT, Claude and Gemini, across the "best X under Y" and "top brands for Z" prompts your actual shoppers run, against the competitive set that actually matters in your category and price tier. That is what our AEO monitoring platform does, and it is the starting point of every engagement we run.

Yes, and it is one of the more straightforward GEO fixes once the underlying causes are identified. Attribute inaccuracies usually come from three places: thin or poorly structured product detail pages on your own site, outdated or inconsistent attribute fields on retailer listings, and competitor or editorial content that describes your products with old information. The fix is a combination of rebuilding product pages with structured attribute blocks, refreshing retailer and review platform listings, publishing citable attribute-focused content and seeding accurate descriptions through trusted third-party channels.

Because the price tier signal across your website, retailer listings, editorial coverage and review platforms is reading as a different tier to language models than the one you actually operate in. AI tools infer price positioning from the language used about your brand across many surfaces, and when those signals point in different directions, models default to the tier with the most data attached to your name. Realigning the price tier signal across owned and external surfaces, and getting the right tier of comparison content in front of language models, is one of the highest-leverage GEO interventions for retail brands.

By refreshing the language used about your brand across the surfaces AI tools cite most heavily. Brand descriptors travel with every AI recommendation, so they are worth tracking deliberately. The mechanism is to identify which descriptors are currently dominant, where in the source landscape those descriptors are coming from, and work to introduce current brand language through review platforms, editorial partners, refreshed on-site content and trusted third-party coverage. Sentiment shifts in AI search take longer than attribute fixes, but they compound, and the brands that monitor and manage descriptors deliberately end up with a significant edge.

Both, and the boundary is moving fast. A growing share of retail purchase journeys now begin with an AI query. "Best noise-cancelling headphones under €200." "Most reliable robot vacuum for pet hair." "Top sustainable skincare brands for sensitive skin." Shoppers increasingly use AI tools to build a consideration set first, then go to a marketplace or brand site to verify, compare and buy. Brands that appear consistently in those AI-generated shortlists get discovered earlier, considered more often and converted on better terms than brands that have to be discovered downstream on a marketplace search results page.

Some changes show up in AI search within weeks. Product attribute corrections, structured content updates and retailer listing fixes often surface in AI responses within four to eight weeks once language models re-index the affected sources. Deeper shifts, like building category recommendation presence from a low starting point or correcting persistent price tier misattribution, typically take eight to sixteen weeks of sustained work. Most retail engagements we run start showing measurable movement on category presence, attribute accuracy and Share of Voice inside the first 90 days.

Every engagement starts with AEO monitoring tailored to your category, price tier, shopper personas and competitive set, run through our in-house platform. From there we audit your GEO readiness across three layers (technical foundations, content and product documentation, third-party presence across retailers, reviews and editorial), identify the sources that are actually shaping AI answers in your category, and deliver a prioritised 90-day GEO roadmap. Then we execute, either alongside your team or as a fully outsourced function. The work covers website and semantic structure, product detail and category pages, shopping feeds, AI-oriented content, review platform visibility, editorial and authority building, community presence and ongoing optimisation as AI search behaviour shifts.

You can run parts of it in-house, particularly if you already have strong content, brand and SEO functions. What is harder to build internally is the monitoring layer, the cross-LLM diagnostic capability and the pattern recognition that comes from running GEO across multiple retail categories and price tiers. Most of our retail clients run a hybrid model. We bring the monitoring platform, the diagnostic work and the category benchmarking, their team owns the product and brand surfaces we recommend changes to, and we execute the external visibility and authority work together. That tends to be the fastest and most cost-effective way to get a retail brand from invisible to consistently recommended in AI search.