AI shopping is 51% of buyers in 2026. Most stores are invisible to it. Find out where you rank in 90 seconds.
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When a shopper types “best yoga mat for sweaty hands under $80” into ChatGPT, they get a specific list of recommended brands. That list is not random. It is drawn from structured product data, third-party reviews, comparison articles, and the specific attributes each AI model has learned to associate with quality in your category.
If your store is not in that list, you are not losing to a better ad bid or a higher PageRank. You are losing because your product data is not machine-readable, your descriptions do not cite the attributes AI assistants look for, and your brand has not earned the citations that AI models use to justify recommendations.
47% of Shopify stores we have audited have a visibility score below 30 — meaning AI assistants almost never recommend them even when their products are directly relevant. This is not a permanent condition. It is a fixable data problem. AISeen identifies exactly what is broken and tells you exactly how to fix it, product by product.
We run 100–2,000 buying queries against every major AI shopping platform daily. Every mention of your brand is logged with its position, the context surrounding it, the sentiment, and the specific reasons the AI cited for recommending you — or your competitor instead.
When Allbirds appears and your wool sneaker brand does not, we extract exactly what ChatGPT said about them — “sustainably sourced merino”, “carbon-neutral shipping”, “machine washable” — and cross-reference against what you are communicating. The gap is your roadmap.
The recommendations engine produces specific, implementable fixes: a rewritten product description with the exact attributes to add, the precise JSON-LD schema block to insert, the comparison article titles to publish. Pro plan users push approved changes directly to Shopify with one click.
Most AI monitoring tools were built for PR teams tracking brand mentions in news coverage. They measure whether an AI chatbot knows your company name. That is not what a Shopify seller needs. You need to know which of your 47 SKUs are getting recommended for “gift for home baker under $60”, and which ones are losing to Williams-Sonoma. That requires e-commerce-specific logic.
Visibility is tracked per product, per query, per AI platform. You can see that your cast iron skillet gets mentioned frequently on Perplexity but never on ChatGPT, while your baking stone appears across all platforms for half the queries you care about. This SKU-level granularity is what lets you prioritize which products to fix first.
Connect your store in under five minutes. We sync your full product catalog automatically, respect your existing schema markup, and push approved fixes back through the same API connection — no manual exports, no CSV round-trips.
Pro plan users can approve a recommended product description rewrite and have it live on their Shopify store in seconds. Every auto-apply creates a backup so you can undo with one click if the change does not perform.
Sample: Coastal Candle Co. — mention rate by AI platform, 30-day view
Install our Shopify app with one click, paste WooCommerce API keys, or connect via Amazon SP-API. We immediately pull your full product catalog — titles, descriptions, prices, images, existing schema markup, and metafields.
Claude Sonnet analyzes your catalog and generates 100–2,000 natural-language shopping queries across nine categories: comparison, gift, problem-solving, feature-specific, budget, use-case, sustainability, alternative-seeking, and long-tail demographic. These are the exact queries your potential customers are asking AI assistants right now.
Each query is sent through a shopping assistant prompt to ChatGPT, Perplexity, Gemini, Claude, and Google AI Overviews. We use consistent prompt templates across providers so results are directly comparable. Responses are stored with full attribution.
Your daily visibility score (0–100) is calculated from mention rate, average position, share of voice versus competitors, and positive sentiment ratio. The recommendations engine then surfaces the highest-impact fixes, ordered by expected improvement.
AISeen is built for e-commerce sellers who move fast and need data, not dashboards full of vanity metrics. If you are running a store between $40k and $5M in annual revenue and you rely on product discovery — not just branded search — this tool is for you.
You have a catalog that AI assistants should be recommending, but you have no way to know if they are. Our Shopify integration gives you visibility and one-click fixes without leaving your workflow.
When ChatGPT or Perplexity recommends a competitor's ASIN over yours, the reason is in the data. We surface it — the specific attributes cited, the review sites referenced, the content gaps that are costing you recommendations.
WordPress flexibility creates inconsistent product data that AI assistants struggle to parse. We audit your schema coverage, identify the gaps, and give you the exact JSON-LD blocks to add.
When a shopper asks ChatGPT for the best sustainable water bottle and Hydro Flask appears but your brand does not, the fix usually is not bigger marketing spend. It is better-structured product data and the right comparison content.
Google is still important. Branded search, local intent, and image-driven discovery still run through traditional search. We are not telling you to abandon SEO.
But here is what is changing: when a shopper does not know your brand yet — when they are in the research phase asking “which yoga mat is best for hot yoga” or “what running shoe works for wide feet and plantar fasciitis” — they increasingly ask an AI assistant before they open Google. And the AI assistant does not look at your keyword rankings. It cites the sources it was trained on or, in the case of live-search models like Perplexity, the pages it can currently retrieve and summarize.
The optimization strategy for AI discovery is different enough that it requires its own toolset. Structured data matters more. Review site coverage matters more. The specific attributes in your product descriptions matter enormously — not for keyword stuffing, but because AI models extract explicit feature claims to use in recommendations. A product description that says “high-quality material” is invisible to AI. One that says “2.5mm natural rubber base, sweat-wicking microfiber surface, 72 × 26 inch, 6.5 lb” gives an AI assistant something to cite.
AISeen is built on this insight. The recommendations engine does not suggest generic “improve your content.” It produces the exact rewrite for each product, referencing the specific attributes your competitors are getting cited for — and that your description is currently missing.
Start free with a one-time public audit. Upgrade to continuous monitoring when you are ready to act on the data. Every paid plan includes a 14-day trial — no credit card required.
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