PAL | Ecommerce Product Listing QA — up to 50 URLs

Stop checking product-page markup manually. Audit up to 50 public ecommerce product URLs in one run, then hand off an exportable fix list.

Designed for ecommerce catalog operators, technical SEO agencies, developers shipping new storefront templates, and teams migrating product pages.

What you get

• One Dataset row per unique URL with retailQaScore, suggestedFixCount, issueCount and the final page URL.
• Actionable fixes, not only warnings: each finding includes severity, a concrete suggested change and a heuristic score deduction.
• Product JSON-LD evidence: published Offer price and currency, availability label, product name, brand, SKU and image presence.
• Checks for missing or inconsistent Product/Offer metadata, plus canonical URL and meta description.
• Export to JSON or CSV directly from Apify for product QA handoffs, migration reviews or subsequent rechecks.
• Up to 50 URLs per run, with at most two pages fetched concurrently to keep resource use controlled.

Try it with real example pages

Copy into the Input tab:

{
  "urls": [
    "https://scrapeme.live/shop/Pikachu/",
    "https://scrapeme.live/shop/Bulbasaur/"
  ]
}

Press Start, then open the Dataset tab. Sort by suggestedFixCount, or inspect the details.suggestedFixes array to see exactly what should be reviewed.

Verified sample: On 19 October 2026, a cloud test of the two URLs above returned two successfully audited rows. At that time both had a heuristic retailQaScore of 89, with review items for missing brand and meta description markup. These results are time-dependent; your run may differ if the public example pages change.

Example of the useful part of one result:

{
  "status": "audited",
  "retailQaScore": 89,
  "suggestedFixCount": 2,
  "details": {
    "suggestedFixes": [
      {"code": "brand_missing", "suggestedFix": "Include brand when known."},
      {"code": "meta_description_missing", "suggestedFix": "Add a descriptive HTML meta description when useful for the listing."}
    ]
  }
}

The excerpt above is condensed. The dataset provides source/final URLs, timestamps, issue details and a per-product Offer summary.

When to use it

Before a launch: audit representative new product pages for missing JSON-LD fields and broken metadata.

After a migration: submit a batch of up to 50 product URLs and export a review list for your development team.

During catalog maintenance: run on the same pages after template changes to check whether previously identified issues were addressed.

Pricing and limits

Pay-per-event: a pal-availability event is charged for each successfully audited public HTML page, even when no issue is found. Fetch failures are emitted as error rows without a successfully processed-page event. Duplicate input URLs are checked once. View the Pricing tab for the live per-event price and billing terms before running a large batch.

No storefront login, CMS installation, private credentials, paid proxy or separate AI API key required. The target must be a public HTTPS HTML page containing relevant static markup.

This is a static HTML/JSON-LD audit, not a Google ranking score, legal compliance assessment, checkout verification, live inventory check or JavaScript-rendering crawler. A flagged field can be intentionally absent; human review is recommended. Maximum response body: 1.5 MB per page; network timeout: 11 seconds per fetch.

Continue with another PAL tool

• PAL Competitor Price & Stock Change Monitor: compare published product prices and stock labels across runs.
• PAL Product Price & Stock Snapshot: extract publicly published catalog price and availability data.
• PAL Product Image SEO & JSON-LD QA: focus on image schema and HTML alt metadata.

Practical AI Lab (PAL) — product QA tools with exportable, inspectable findings.