The AI visibility platform that sees every SKU.

Every SKU, every engine, every week.

eCommerce Insights watches your catalog the way buyers do — through the AI engines they actually use. Each scan runs a rotating query bank across six engines and records which SKU each one cited, what sources it cited alongside you, and how the engine phrased the recommendation.

• Six engines on every plan. ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude, Copilot. Rufus and Sparky on request.
• Category-tuned query banks. eCommerce Insights generates buyer-shaped queries from your collections and best-sellers, then rotates them each scan so you aren't tracking the same ten phrases forever.
• Change-over-time charts. Per SKU, per engine, per query. See when a competitor replaced you in ChatGPT's answer and on which query it happened.
• Query-intent segmentation. Separate "best" queries from "cheapest," "gift for," and "alternative to." The AI behavior differs; the reporting should too.
• Competitor cross-reference. Every citation surface records who else was cited. You see the real competitive set — not the one your marketing deck says it is.

Competitor cross-reference: Early access

An AI-readability score for every PDP.

The eCommerce Insights Score is a 0–100 composite built from five factors. Every SKU gets one, refreshed with every scan, with every sub-score visible. The score exists so a merchandiser can sort the catalog by "most broken for AI" and work the list top-down.

1. Structured data completeness. Product schema coverage across name, brand, GTIN, SKU, description, image, offers, aggregateRating, material, color, size. Missing fields hurt entity resolution.
2. Citation surface. How often the SKU is cited relative to catalog peers and tracked competitors on the query set we scan.
3. Entity clarity. How well the PDP disambiguates your product from similar products — brand name consistency, canonical URL, title specificity.
4. Answer coverage. Whether the PDP body answers the top buyer questions for the category (size guidance, material origin, care, comparisons, returns).
5. Review signal. Volume, recency, and structured-data surfacing of user reviews. Low review schema coverage is a common failure mode.

Read the full score methodology →

Concrete changes, pushed to Shopify.

For every SKU below the threshold you set, eCommerce Insights produces a diff. You see the proposed new title, description, bullet set, schema block, and metafield values side-by-side with the current ones. A merchandiser reviews, edits, and approves. eCommerce Insights then writes the change — or hands you the CSV.

• Diff-style recommendations. Proposed title, meta description, bullet list, body HTML, schema fields. Changes shown in context.
• Metafield support. eCommerce Insights recommends values for GTIN, brand, material, pattern, and the rest of the Product schema — and writes to the right metafield namespace.
• Bulk actions. Approve multiple similar diffs in one pass. Filter by category, collection, score delta, or engine-specific gap.
• Shopify push. One click from approved diff to live PDP, via the Shopify admin API.
• Change log and rollback. Every push is logged with the approver, the diff, and a one-click rollback.
• Recommendation-only mode. Don't want eCommerce Insights to write to Shopify? Run in read-only mode and ship changes through your existing workflow.