Ecommerce Marketing Agency for AI Shopping Answers

Shoppers used to search, open five reviews and read a comparison table. Now many ask an AI engine for the best running shoe for flat feet under £120 and get three products with a reason for each. Google AI Mode and ChatGPT go further, showing product cards with prices, ratings and places to buy.

For a DTC brand or online retailer, that moves the point where the sale is won. The shortlist is settled before anyone reaches your product page, and a wrong price, a missing size range or an old complaint can keep you off it. We are an AI visibility agency that works prompt by prompt: we measure which products the engines recommend in your category and fix the data and sources behind those answers.

What shoppers ask AI before they buy

• “Best cordless vacuum for pet hair in a small flat”
• “Is [your brand] or [rival] better for sensitive skin?”
• “Best espresso machine under £400 for a beginner”
• “Carry-on suitcase under $200 that fits overhead bins on most US airlines”
• “Sustainable activewear brands that use recycled fabric and ship to Canada”
• “Alternatives to [rival] mattress with a longer trial period”
• “Protein powder with no artificial sweeteners that tastes good with water”
• “Gift for a 10-year-old who likes science, under $50”

Each prompt carries a budget, a constraint or a comparison. Engines match those details against what they can read, so a product page that never mentions pet hair, small spaces or trial length is easy to leave out.

Where AI shopping answers come from

Product recommendations typically draw on:

• Independent reviews and tests: Wirecutter-style buying guides and specialist review sites that test products side by side.
• Comparison listicles: “best X for Y” articles, which engines often cite directly.
• Owner discussion: Reddit threads and YouTube reviews, where real owners describe what lasted and what broke.
• Marketplaces and review platforms: Amazon reviews and Trustpilot, read for recurring themes as much as star ratings.
• Structured product data: Google says its AI Mode shopping experience is built on its Shopping Graph, and Merchant Center is how retailers send Google their product data. OpenAI publishes a product feed specification for sellers who want their products discoverable in ChatGPT.
• Your product pages: where engines check specs, sizes, materials, prices and policies.

Why strong products get left out of AI shopping answers

Product pages written for browsing, not answering. Lifestyle copy gives an engine little to quote, and specs behind tabs, size charts saved as images and care details in PDFs are hard for it to read.

Feed and page disagree. When the price, stock status or variant names in your feed differ from your product page, engines and shopping surfaces have two versions of the truth and may trust neither.

Absent from the comparisons that get cited. If the listicles an engine keeps citing for your category do not include you, the answer usually will not either.

Old review themes that will not go away. A sizing complaint fixed two years ago can still shape how an engine sums up your product, because older reviews and threads are the ones it finds.

How we help ecommerce brands get recommended by AI engines

What working with us looks like

We start from your catalogue. We pick the categories and products that matter most this year with your ecommerce lead, measure how seven engines recommend in those categories, then fix the product data, feeds and third-party sources behind the answers.

What we deliver

• A prompt map by category, need and price band. Best-for, head-to-head, alternatives and gift prompts for each priority category, with the details shoppers add: budget, size, skin type, compatibility, delivery country.
• Answer-ready product pages. Specs, materials, sizes, care, delivery and returns in the page text, size charts as HTML tables rather than images, and Product structured data with offers, availability and ratings that match the page.
• A feed and page reconciliation. A product-by-product check of your Merchant Center feed against your store, covering titles, GTINs, variant names, prices and stock status, with a ranked fix list.
• Buying guides built from your specs. Pages that answer the comparison questions shoppers ask, such as which model suits a small flat or a first-time buyer, and say plainly who each product is not for.
• Review and comparison placements. The review sites, listicles, YouTube channels and Reddit communities each engine cites for your categories, then samples for reviewers and pitches to editors. We never post fake reviews or undisclosed promotion.
• A fact-correction log. Wrong prices, discontinued products, old sizing complaints and marketplace listings that contradict your site, each traced to its source with the fix and the date it changed.
• Monthly answer share by category. Whether each product is named, where it sits in the list and at what price, against the brands you name as rivals.

Your first 90 days

Weeks 1 to 2: map and measure

• Priority categories, products and rivals approved by your ecommerce lead, and the prompt map agreed.
• A baseline across seven engines for every prompt in the map.
• The feed audited against product pages, and wrong facts and technical blockers ranked, such as specs behind tabs or variant URLs that do not crawl.

Weeks 3 to 6: fix the data

• Feed fixes made in Merchant Center, and the first answer-ready product pages live, starting with best sellers that engines leave out or misdescribe.
• Wrong prices and discontinued products corrected at the source, including your own marketplace listings.
• Reviewers and listicles shortlisted, and the products to offer for testing agreed.

Weeks 7 to 12: earn the comparisons

• Samples and pitches out to the reviewers and list authors engines cite, and the first buying guides published.
• The prompt set re-run against the baseline. The month 3 checkpoint is movement on target questions and the first earned citations quoted by engines.

By month 6 you see answer share by category, engine by engine, against named rival brands. Our method shows how the four steps fit together.

What you see each month

The report shows, category by category, which products each engine recommends, in what order, at what price and with what reason to buy, and which pages it cites. It flags answers that quote a price or stock status different from your store. The live dashboard holds the same answers, so merchandising can check a launch in its first week. Our guide to measuring brand visibility in ChatGPT explains how a fixed panel of prompts is built and scored.

Illustrative example, not client data:

What we need from you

• An approver: your ecommerce lead, with merchandising checking product copy and your compliance owner seeing any health, sustainability or performance claim.
• Access: your store platform, Google Merchant Center, Search Console and analytics, plus read access to your Amazon listings if you sell there.
• Priorities: the categories and products to push, including launches and lines you are winding down.
• Rivals: the brands you lose sales to in each category.
• Samples: a small allocation of stock for reviewers, where testing decides who gets listed.
• Approvals within a few working days, so fixes land before a sale or a launch.

Plans start at $2,000 a month, with custom engagements for a narrower scope from $1,500 a month. Our pricing sets out what each plan includes.

Product feeds and AI shopping surfaces

The product cards in Google AI Mode and ChatGPT lean on structured product data, so your feed is now part of how you are recommended, not just an advertising input.

A feed that helps has titles that say what the product is, complete attributes such as size, colour, material and GTIN, accurate availability and prices that match the product page. Variants need names a shopper would recognise, not internal codes. OpenAI's product feed specification covers many of the same fields, so a clean Merchant Center feed is a sound starting point for ChatGPT as well.

Because Google results still feed many AI answers, the technical groundwork comes with the work: crawlable product pages, sensible handling of variants and filters, and robots rules that let AI crawlers reach your catalogue.