Product, collection and comparison pages rebuilt so assistants can read your catalogue, understand your differences and recommend your products by name.
Assistants recommend products the way a careful shop assistant does: by matching stated constraints to known attributes. That means the brands that win are the ones whose attributes are explicit, structured and consistent everywhere they appear — on the page, in the schema, in the feed and in third-party listings. Most catalogues fail on consistency rather than depth. We fix the attribute layer first, then rebuild collection and comparison pages around the constraint language buyers actually use.
An attribute you did not state is an attribute the model will assume you do not have.
— Arise GEO, Ecommerce AI Search Optimization
Your catalogue is a database. Publish it like one.
— Arise GEO, Ecommerce AI Search Optimization
We build an attribute model for your category — the dimensions buyers filter on and assistants match against — then close the gaps across page copy, structured data and feeds.
A one-sentence answer to 'what is this and who is it for', explicit constraints, honest exclusions, and structured specifications a model can lift without ambiguity.
Instead of one page per category, pages per buying constraint — the phrasing buyers actually give an assistant — each with genuine editorial guidance rather than a filtered grid.
Assistants lean heavily on comparison and alternatives content. We build yours, with real specifics and fair treatment of competitors, because models discount obvious puffery.
We can promise this because our methodology already works. Hundreds of pages now sit in position #1 across our clients' catalogs. The risk shouldn't be on you — it's on us.
Shopify and Shopify Plus most often, plus BigCommerce, WooCommerce, Magento, headless builds on Next.js or Hydrogen, and custom stacks. The attribute and schema work is platform-agnostic; only the implementation path changes.
We have run this on catalogues from 200 SKUs to just over 400,000. Large catalogues get a template and rules approach rather than page-by-page editing, plus a sampling QA process.
Yes. Attribute depth, schema validity and constraint-shaped collection pages are all strong classic ranking signals. Most clients see organic recovery before they see citation lift.
Yes, because feed and page disagreement is one of the most common causes of poor recommendation quality. Feed work is included in the attribute parity stage.
Forensic audit at $4,500, then a monthly engagement typically between $6,500 and $14,500 depending on catalogue size, locales and whether we implement. Implement our 90-day plan and if you don't see movement in 3–6 months, we refund our fees in full.
Three layers, in order. First, entity clarity: one canonical Organization with @id anchors, sameAs links to authoritative databases, and knowsAbout coverage of your real expertise. Second, extractable content: atomic answer blocks, comparison tables, transparent pricing and specs, and FAQ blocks on every commercial page. Third, corroboration: mentions in the roundups, directories, review platforms, and trade publications those engines retrieve. Measure it by probing a fixed set of buying questions monthly and tracking citation frequency per engine.
Citability. AI engines lift passages that answer one question completely in 40–80 words, in plain language, with a concrete number, definition, or list. Pages that bury the answer under narrative rarely get quoted. We rewrite key pages so every important question has a self-contained answer block, wrapped in schema that labels what it is.
Yes, because AI answers are built from indexed pages. Every major assistant retrieves from a crawled index, so pages that aren't crawlable, renderable, or structured never enter the candidate pool. Classic SEO is now the entry requirement for AI visibility, and the two programs share almost all of the same work.
Technical recovery and on-page work typically show movement in 30–60 days. AI citation lift on long-tail and comparison queries usually lands in 30–90 days. Category-level authority and head-term rankings are a 6–12 month curve. Every engagement ships a sequenced 90-day plan so early wins fund the long work.
You get a forensic audit, a prioritized 90-day execution plan with effort and impact scoring, and optional done-for-you implementation by our engineers and editors. Pricing is scoped per engagement based on catalog size, locale count, and whether you want execution included. Implement our 90-day plan and if you don't see results in 3–6 months, we refund you in full.
Audits scoped within 24 hours. Results within 90 days, or your money back.
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