AI SEO for E-commerce

Normal ecommerce SEO focuses on ranking your product and category pages in Google's results — titles, descriptions, internal linking, Product schema, page speed and crawl control. AI SEO for e-commerce includes all of that and adds a second layer: making your products and categories part of the answers AI assistants like ChatGPT, Gemini, Google AI Overviews and Perplexity give when shoppers ask for comparisons and "best for" recommendations. The AI layer is measured as a baseline — how often engines name your products versus competitors, reported as share of voice. Both layers reinforce each other: the fundamentals decide whether engines can crawl and understand your catalogue at all, while the AI-visibility work decides whether you're named in the comparisons shoppers increasingly rely on before they click.

They tend to ask in a few recognisable patterns, and each is a chance to be named. There are "best [category] for [use case]" questions, like best office chair for lower back pain; direct comparisons, like X versus Y for a specific need; constraint questions that add a budget, a material or a compatibility requirement; and attribute questions about specifics like waterproofing, sizing or shipping regions. To appear in those answers, your product information has to address the exact use cases, constraints and attributes shoppers name, in clear language an engine can extract. Vague copy like "premium quality" gives an engine nothing to quote, whereas a concrete spec, a stated use case and an honest limitation give it exactly what it needs to cite you.

Product schema is structured data that tells engines, in a machine-readable form, the facts about each product — price, availability, currency, brand, identifiers like GTIN or SKU, shipping details, and genuine ratings or reviews. Clean, accurate schema helps both classic search features and AI systems read your catalogue correctly rather than guessing from messy page text. The critical rule is that structured data must match the visible page: if your schema claims a price, rating or availability the page doesn't show, engines may ignore the markup or distrust the source. So we don't simply bolt schema on top of stale pages; we align the structured data with accurate, current page content, which is what actually improves how engines understand and cite your products.

Reviews are a real input to both classic SEO and AI citation, because engines lean on reputable third-party sources and on genuine on-page rating data. Strong, authentic reviews on platforms your category's buyers trust, plus valid review schema where real ratings exist, can help you get surfaced. What we will not do is fabricate reviews, buy fake ones, or incentivise dishonest ratings — that violates platform rules, risks penalties, and ultimately erodes the trust signals you're trying to build. Our approach is to help you collect authentic reviews properly, surface them correctly on-site, and build a presence on the review platforms the engines and your buyers actually rely on. Genuine review signals compound over time; faked ones are a liability.

Marketplaces are part of your discoverability footprint, not separate from it. An AI engine may encounter your product on your own store, in a marketplace listing, and in third-party reviews or roundups, and consistency across those places tends to help while contradictions can hurt. We check that your product feed — Google Merchant Center and any marketplaces you use — is healthy and consistent with your live store pages, so the same product doesn't show different prices, titles or attributes in different places. We also consider how marketplace presence and your own-store presence reinforce each other: sometimes a marketplace listing earns citations your store hasn't yet, and sometimes your own detailed product page is the stronger citable source. The aim is a coherent footprint engines can trust.

We measure product and category share of voice — how often AI engines name your products versus a fixed set of competitors across a consistent prompt set — plus your mention rate and citation rate, and the mix of source types engines pull from in your category. Because AI answers are non-deterministic and vary by run, user and location, we run each prompt multiple times and report dated ranges rather than a single misleading snapshot. Where you grant analytics and Search Console access, we also connect this to organic sessions, product-page impressions and clicks, and leads or sales on the relevant pages, so the work ties back to revenue rather than to vanity metrics. Every monthly report ends with numbers and a clear next-month action list.

It depends on catalogue size and competition. Smaller stores with a focused range often start on Starter (around $500/month): baseline AI-visibility tracking plus a steady stream of high-impact product and category fixes. Growing stores with many SKUs that need sustained content, schema and review work usually fit Growth (around $1,500/month). Large catalogues, multiple markets or languages, and highly competitive categories tend to need Enterprise ($3,500+/month). Pricing is in USD and is confirmed before any engagement. If you're unsure, the free AI SEO audit will recommend a tier based on what your store actually needs — including "fix a few things yourself first" when that's the honest answer — and the package recommender can point you in the right direction quickly.

Yes, but it's prioritised rather than brute-forced. Trying to hand-optimise every SKU at once is rarely the best use of budget; instead we identify the categories and products that drive the most opportunity — high-margin lines, competitive "best for" queries, and pages already close to being cited — and work through them in impact order. For large catalogues we also lean on structural fixes that benefit many pages at once: faceted-navigation and crawl control, templated but genuinely useful product-description patterns, consistent Product schema applied across the catalogue, and feed consistency. The AI-visibility testing focuses on the prompt sets that matter most to your revenue. This is exactly the kind of scope where Growth or Enterprise fits, and where the audit's prioritisation earns its keep.

No, and we won't pretend otherwise. AI engines decide what to recommend using models and sources outside any agency's control, their answers vary by run, user and location, and Google's rankings depend on competition and many factors beyond one store. What we can do is improve and measure the inputs that make citation and ranking more likely — extractable product content, accurate Product schema, consistent feeds, genuine review signals, crawl access and a trusted footprint — and report honestly how your visibility moves against competitors. Sales also depend on factors we don't control, like pricing, product quality and checkout experience. Our commitment is a transparent methodology and measurable progress in visibility, not a guaranteed revenue figure that no legitimate provider can promise.

Shopping ads buy you placement for as long as you pay, and they stop the moment the budget does. AI SEO builds visibility you own: product and category content, structured data, feed consistency and a citation footprint that keep working in organic results and AI answers without a per-click cost. The two are complementary rather than competing — ads can drive immediate traffic while AI SEO compounds over months — but they're not interchangeable. Importantly, AI assistants assembling a "best [product]" answer generally pull from organic and third-party sources, not from your ad spend, so being named in those answers is something you earn through visibility work, not something you can buy directly. We'd rather you understand that distinction than confuse paid placement with earned citation.