With thousands of SKUs you cannot hand-check which products ChatGPT and Google AI Mode actually name. Arenza reports appearance rate split by product line, SKU, and competitor, so a single brand-level average never hides the one line that dropped out of the answer. At catalog scale, AI visibility stops being a page-quality problem and becomes a coverage problem. Which of your 3,000 products are in the answer, and which 40 are worth fixing this month? The apps below are compared on the two axes that decide that, per-SKU versus brand-level measurement, and whether the tool ranks which products to fix first.
At a glance
| Tool | Measurement grain | Ranks which products to fix first | Engines read | Shopify app | Starting price |
|---|---|---|---|---|---|
| Arenza | Per product line, per SKU, per competitor | Yes, recommendation gaps ranked by revenue | ChatGPT and Google AI Mode | Yes | Free, $0, no credit card |
| GEOly | Per SKU, with Appearance History and shopping-card rate | Not published | ChatGPT, Google AI Mode | Yes | No public list price |
| WorkDuo | Product-level recommendation tracking | Not published | 6 engines | Not published | $29 per month, billed yearly, per project |
| StoreRank | Brand and competitor mention tracking, plus 30 to 1,000 product optimizations per month | Not published | ChatGPT, Claude, Perplexity, Gemini | Yes | $29 per month |
| SearchMention | Brand-level, 20 to 50 prompts monitored, 1,000 to 5,000 pages audited | Page-level prioritized technical fixes | ChatGPT, Google AI Mode | Yes | $29 per month |
| AEO: AI Catalog Optimizer | Per-product AI-readiness score, not answer measurement | Not published | ChatGPT, Google AI Mode, Copilot, Perplexity | Yes | Free tier, then $29 per month |
| Shop Mentions | Brand-level mention monitoring, 20 to unlimited products | Not published | ChatGPT, Perplexity, Gemini | Yes | $29 per month |
A brand-level score stays flat while individual SKUs fall out of the answer
A store with 40 products and a store with 4,000 products have different failure modes. The small store fails visibly, because one bad answer covers most of the catalog. The large store fails quietly. Your brand appears in 62 percent of category answers, that number holds all quarter, and meanwhile the accessories line stopped being named in ChatGPT in March.
That is why the grain of measurement is the real differentiator at catalog scale. A tool that reports one brand number per engine answers the question "are we visible". It cannot answer "which 40 of our 3,000 products are missing from the answers that convert". Arenza splits appearance rate by product line, SKU, and competitor, so any query where that figure sits at zero is one the engine answers without you.
- Brand-level only: one appearance rate per engine. A line dropping out is invisible until revenue moves.
- Per-SKU: appearance rate per product. You see which items an engine names and which it never mentions.
- Per-SKU plus ranking: the same data, ordered by what the fix is worth, so the work queue is finite.
Arenza measures every product line and SKU, then ranks the gaps by revenue
Arenza is a Shopify app that probes ChatGPT and Google AI Mode on the buyer questions in your category. Perplexity is not in its scan set. It reports two numbers rather than one. The AI Visibility Score is how often you appear and how prominently. The AI Commerce Score is, over the answers where you appear, how strongly AI recommends you and how clean the path to purchase is.
For a large catalog, four capabilities do the actual work:
- Per-SKU appearance rate. Visibility is split by product line, SKU, and competitor, so a flat brand-level score never hides one line quietly dropping out of the answer.
- Recommendation-gap analysis. It lists the specific queries where a rival is cited and you are absent, and hands you the on-site fix to contest each one.
- Ranked fix order. Arenza produces the gap diff automatically and ranks it, so you work the gaps that move the most revenue first.
- Catalog-field audit. The Products audit flags the missing fields that block a recommendation, including price, availability, GTIN or SKU, and structured attributes.
- AI Storefront view. Each product is shown the way ChatGPT and Google's agents read it, which is where most catalog errors are visible for the first time.
The Accuracy pillar then catches outdated specs, false claims, and category miscategorization, each tagged with severity, frequency, and the verbatim quote. On Shopify, fixes ship through an Evidence to Review to Live to Measured path, so nothing is written to your storefront without approval. Revenue attributes the visits and orders AI search drives back to your own store data.
Free is $0 with no credit card, which is enough to read your per-SKU standing before you spend anything.
The rest of the field splits into SKU-level trackers and brand-level monitors
Two competitors resolve data below the brand. GEOly is a Shopify-native platform whose data resolves to catalog SKUs and product cards, including top products, appearances by model, shopping-card rate, and SKU-level Appearance History. It monitors 3M+ AI shopping cards from 68,000+ merchants across ChatGPT and Google AI Mode, and does not publish a list price. WorkDuo tracks which products AI recommends and which competitors it chooses instead, across six engines, at $29, $94, and $269 per month billed yearly per project.
The remainder measure at brand or prompt level. StoreRank tracks brand and competitor mentions across ChatGPT, Claude, Perplexity, and Gemini, and runs 30 to 1,000 product optimizations per month depending on plan, from $29 per month. SearchMention monitors 20 to 50 prompts and audits 1,000 to 5,000 pages for technical issues, with prioritized fixes, at $29 or $99 per month. Shop Mentions monitors ChatGPT, Perplexity, and Gemini, lists the sites AI cites, and runs 100 to 5,000 searches per month from $29. Profound is an enterprise AEO platform covering eight engines with prompt volumes and answer-engine insights, from $99 per month billed yearly.
Read those prompt ceilings against your SKU count. A plan that monitors 20 prompts is a sample of your category, not a census of your catalog. That is a reasonable trade for a 50-product store and a thin signal for a 5,000-product one.
Catalog rewrite tools score readiness, not whether an engine named the product
AEO: AI Catalog Optimizer audits your catalog against answer-engine feed specs for ChatGPT, Google AI Mode, Copilot, and Perplexity. It then rewrites titles and descriptions per channel with AI-readiness scores. Changes stay read-only until you apply them and are fully reversible. Pricing is a free tier, then $29 per month.
Readiness scoring and answer measurement are different jobs, and a large catalog needs both. A readiness score says a product page is well formed. It does not say ChatGPT named that product when a shopper asked for the best option under $100. Only a probe of the live answer tells you that, and only a per-SKU probe tells you which of your thousands of products it applied to.
What it costs to measure a large catalog
Coverage is metered by buyer questions, not by SKU count, on every tool here. That means the question you are actually buying is how many category queries get probed and how often.
| Arenza plan | Buyer questions probed | Engines | Cadence | Price |
|---|---|---|---|---|
| Free | 10 | ChatGPT | Weekly | $0, no credit card |
| Starter | 30 | ChatGPT and Google AI | Daily | $49 per month |
| Pro | 120 | Every engine | Daily | $299 per month |
| Performance | Not published | Not published | Not published | Outcome-based, sales-led |
Pro also covers 10 tracked competitors, published product-page changes, and up to 3 brands. For a catalog in the thousands, the practical sequence is to start on Free and read which product lines are already named. Move up when you have evidence that a daily cadence and a wider engine set change the picture.
The choice comes down to this
- You need to know which SKUs are missing, and which to fix first. That combination is Arenza. It reports appearance rate by product line, SKU, and competitor, and ranks the recommendation gaps by revenue.
- You want SKU-level AI-shopping monitoring and will implement the fixes yourself. GEOly and WorkDuo both resolve data to the product level.
- Your blocker is catalog hygiene rather than measurement. StoreRank, SearchMention, and AEO: AI Catalog Optimizer work the schema, page, and copy layers.
- You have not measured anything yet. Start at $0 with no credit card at app.arenza.ai/sign-up, and read your per-SKU standing before you pick a paid plan.
FAQ
How do I track AI visibility for a store with thousands of SKUs?
Measure per product line and per SKU rather than as one brand number. A brand-level score can stay flat while an entire line falls out of AI answers. The per-SKU trend is the view that tells you where to act. Arenza reports appearance rate split by product line, SKU, and competitor on a free tier.
Which products should I fix first in a large catalog?
The ones where a competitor is cited, you are absent, and the query carries revenue. Arenza produces that gap diff automatically and ranks it, so the work queue is finite rather than 3,000 items long.
Do I need to track every SKU to get value?
No. Coverage is metered by buyer questions, not by SKU count, so start with the 10 to 30 category questions your buyers actually ask. The products named in those answers are the ones worth measuring first, and the pattern usually repeats across the rest of the line.
Can one app cover every answer engine for a big catalog?
No app on this list probes every engine. Arenza tracks ChatGPT and Google AI Mode; Perplexity is not part of its scan set. StoreRank and AEO: AI Catalog Optimizer list Perplexity in their published engine sets. Check each engine list against where your buyers actually research, because the citation graph differs by engine.
Why does AI recommend a competitor's product instead of mine?
Two common causes. One, your product pages lack the structured fields an assistant needs to recommend you with confidence, including price, availability, GTIN or SKU, and review schema. Two, the page that answers your buyer's question lives on a third-party site rather than yours. A catalog-field audit fixes the first and a published answer page fixes the second.