Drop a store URL below. Rank Sniper reads the live catalog, the robots.txt and the product schema, then scores exactly how much of it a shopping agent can parse. Nothing installed. Nothing stored.
You install it like any other app. Standard OAuth on Shopify’s own screen. No password, no staff account, no collaborator code, nothing to export.
One click from the App Store or a custom link. You approve the scopes on Shopify’s own consent screen and can revoke them from your admin at any second.
Full catalog pulled through the Admin GraphQL API — variants, metafields, media, inventory, policies. Minutes, not days, and no slowdown on your storefront.
Enriched fields land in a dedicated ranksniper metafield namespace. Your titles, descriptions and copy are never touched. Uninstall and your catalog is byte-for-byte what it was.
A theme app extension injects JSON-LD into your product pages, and a hosted endpoint serves the same canonical record to anything that asks for it.
Daily scans catch new and changed products. Weekly scans catch tag drift and schema stripped by theme updates. Monthly checks re-verify crawler access. Included for life.
Left is what's live in the store today. Right is what Rank Sniper publishes — same product, same truth, restructured so a machine can act on it. The block underneath is the literal payload an agent receives.
The same record moves all four, for four different reasons. Every line below points at a field you can watch change when you drag the divider.
A product with no type and no attributes joins no comparison set — it is not ranked badly, it is not in the running. Populated fields are what put it in one.
An answer engine needs something quotable. A named weave and a stated fit can be lifted as an answer; an em dash cannot, so the engine paraphrases the page and the citation goes elsewhere.
A generative engine attributes claims to sources it can verify. A GTIN and a live stock state give it something to stand behind; without them it prefers a competitor that has them.
An agent that cannot resolve price, availability and delivery can describe the product but not transact. These three fields are the difference between a listing and a purchase.
Move the sliders to your numbers. The model uses the observed channel: agent-referred sessions growing off a low base, converting roughly 40% better than organic, scaled by how much of your catalog is actually eligible to be surfaced.
If the agent channel matured to 6% of your revenue and conversion held at today’s rate, this is the monthly difference between the records an engine can use now and after. That 6% is an ASSUMPTION, not a measurement: our launch benchmark is not run yet, so no evidenced uplift figure exists. Treat it as a scenario to argue with. The one number here that is not modelled is Growth at $299/month.
A monthly subscription, banded by catalogue size. All four pillars are in every tier and every billing period — SEO, AEO, GEO and AIO are never sold separately. Annual billing is 20% off and is the only discount.
A one-time, lifetime-access tier for the first fifty stores. It is not a catalogue band and it does not renew. $2,999 buys Growth for the life of the store — ten months of Growth, paid once. It is not on sale yet: it goes live only when the launch benchmark has been measured against a real catalogue, because a Founders buyer has no renewal at which we could ever correct a mistake.
Every plan covers all four pillars — SEO, AEO, GEO and AIO. The band decides how big a catalogue we read and how often, never which pillars you get. Thirty-day money-back on every one of them.
Which agents hit your store, what they asked for, which products surfaced, what converted.
Beyond your annual allowance. Bulk-priced past 2,000. Charged only on products processed.
For flash drops and live inventory. New products readable within the hour, not overnight.
Your four pillar scores against the stores ranking for your top queries, refreshed weekly with the gap list.
A canonical record per market, so an agent answering in French or Spanish surfaces the right variant at the right price.
The same engine across every storefront you run, at a standing discount per store.
FAQPage and HowTo markup generated from your own product copy and support history, so an answer engine has something it can quote and cite.
The same canonical record published to Google Merchant Center, Bing and the agent feed formats, kept in step on every scan.
Add-ons are built against whichever pillar our benchmark says is costing stores the most. New ones are announced in the dashboard first, and every subscriber can turn them on from there without a call.
Ask within 30 days of your first purchase and we refund it in full — any plan, annual included. There is no score to hit, nothing to qualify for and nobody to appeal to, because there is no condition to fail. Every fix already applied stays in your catalogue.
SEO, AEO, GEO and AIO have no agreed meaning across the industry, so Rank Sniper states what it means by each, what it reads, and what it loses you if it stays broken. Definitions you can argue with — and one product that covers all four.
Whether conventional search engines can crawl, parse, and rank the catalogue.
What's at stakeA product with no type joins no comparison set — it is not ranked badly, it is not in the running. Where several URLs serve the same item with no canonical to settle it, an index picks one and drops the rest, so variants a shopper searches for are simply not there.
What we scanTitle and meta length/uniqueness, canonical tags, H1 structure, alt-text coverage, sitemap.xml validity, URL structure, product type, tag richness, Googlebot/Bingbot policy.
What we fixSetting a product type and three or more tags puts each product into a comparison set instead of leaving it uncategorised. A valid sitemap.xml and one self-referencing canonical per page remove the duplicate-URL ambiguity that keeps variants out of an index.
Whether the catalogue can be lifted as a direct answer to a shopper's question.
What's at stakeAn engine that cannot find a quotable answer summarises the page in its own words instead. The shopper still gets the answer; the store loses the attribution, the citation and the click that follows it.
What we scanFAQPage and HowTo schema, question-shaped headings, answer-first paragraph structure, list and table density, description depth.
What we fixA description past ~180 characters that opens with a declarative sentence and carries a list or table gives an answer engine something it can quote. FAQPage or HowTo markup turns existing support copy into a citable answer rather than prose an engine has to summarise.
Whether generative engines will cite this store when composing a recommendation.
What's at stakeA disallowed agent is a hard exclusion, not a ranking penalty — the store is absent from the answer rather than low in it, and no amount of catalogue quality compensates. Where there is no vendor and no date, a model has nothing to attribute a claim to and prefers a source that does.
What we scanllms.txt presence and quality, per-agent policy for GPTBot / ClaudeBot / PerplexityBot / Google-Extended, quotable claim density, named entities, dates, vendor and provenance attribution, freshness (updated_at).
What we fixPublishing llms.txt states your terms to generative crawlers instead of leaving them to infer. Naming a vendor and keeping updated_at current give a model something to attribute the claim to and a reason to prefer the record over a stale one. Unblocking an agent in robots.txt is the one change that removes a hard exclusion rather than improving a ranking.
Whether an AI system can correctly parse and act on the record without guessing. Includes transactability as a named sub-score.
AIO here means machine readability — whether the record parses and can be acted on. It is not the umbrella definition that treats AIO as all four disciplines at once, and it is not the off-site trust definition.
What's at stakeContradictory markup is worse than absent markup: a parser that finds two prices on one page cannot pick one, and an agent that cannot resolve price or availability cannot transact at all. Without a SKU there is no stable way to refer to what is being bought, so the record can be described but not acted on.
What we scanStructured attributes, SKU coverage, variant-level offers, price with priceCurrency, availability, shipping weight, imagery, schema conflict detection, entity consistency.
What we fixA SKU on every variant, offers carrying both price and priceCurrency, and an explicit availability value are what let an agent quote and transact rather than merely describe. Shipping weight is what turns a listing into a delivery estimate. Resolving a schema conflict matters more than filling a blank field: contradictory markup is worse for a parser than absent markup.
Rank Sniper does not score backlinks, domain authority, or any link graph. They are conventional-SEO artefacts and have nothing to do with whether an agent can read your catalogue. Every credible source for that data is a third-party index we would have to buy and re-sell, and the numbers would not move a single one of the checks above. Shipping an estimate we could not substantiate would make Rank Sniper a worse tool, not a more complete one.
Every check below is the one the scorer actually runs — this list is generated from the engine, not written beside it, so it cannot describe a product different from the one you buy. The weight is that check’s share of its pillar.
Rank Sniper works on PRODUCT RECORDS. It does not touch your theme, your blog, your navigation or your page copy, and it is not a site-SEO tool. What it changes is the structured data behind each product — which is what an engine reads when it decides whether your product can answer a question.
Product type and tags are written into your catalogue fields, so each product joins a comparison set instead of sitting uncategorised. Titles, meta descriptions and canonicals are generated per product and served through the theme extension. The sitemap entry follows the record, so a new product is submitted the same day it is published.
Descriptions are restructured to open with a declarative sentence and carry the attributes as a list rather than a paragraph. FAQPage and HowTo blocks are generated from your own product copy and support history — never invented — and served as JSON-LD so an answer engine has something it can quote and attribute.
An llms.txt is published stating your terms to generative crawlers. Vendor, provenance and updated_at are written into the record so a model has something to attribute a claim to. Where robots.txt blocks a generative agent we show you the line and what removing it changes — we do not edit robots.txt for you.
SKU, price, priceCurrency, availability and shipping weight are written to the ranksniper metafield namespace and served as JSON-LD, so an agent can resolve them without guessing. Where two blocks on a page disagree, the conflict is flagged rather than silently overwritten: contradictory markup is worse for a parser than an absent field.