Product data checks
When shoppers ask an AI assistant what to buy, the answer is only as good as the product information it can find. Hasmord's product data audit checks a page against six AI-readiness signals to find the buying questions it can’t yet answer, then lists what to fix first. The six are Hasmord's framework for reviewing product information — not the ranking factors of ChatGPT, Perplexity or any other assistant.
Last updated September 21, 2026
A product data audit looks at one product page, or a catalog, and answers three questions: is the product clearly described, are its claims backed by evidence, and what should change first. That helps AI assistants and shoppers understand and compare the product.
It does not tell you why a particular assistant recommended or skipped your product. Assistants do not publish those reasons.
A good fit for brands and ecommerce teams whose products are compared on specifications, use conditions or evidence; for the catalog and content people who own product pages; for SEO and AEO leads evaluating an information-first audit; and for agencies running product-information checks for clients.
Less useful for teams that only want classic keyword-rank tracking, and for sites with no comparable product or service page.
Each signal is a short check. Together they make up the breakdown of Hasmord’s own readiness score, where the same six appear as Structure, Query coverage, Explainability, Comparison, Trust and Freshness.
Is the product identity clear — brand, model, variant, canonical URL? Are key facts readable as text, rather than locked inside images?
Does the page answer the questions buyers in this category ask: fit, compatibility, size, use conditions and similar?
Are benefits stated as specific facts with conditions, rather than empty adjectives such as “premium”, “best” or “powerful”?
Can a reader tell how this product differs from the alternatives — including what it is not for?
Are warranty, returns, support and the sources behind claims easy to find?
Are price, stock and dated information current, and consistent across everything the page says?
Checks are organised by category, because different products need different facts. Hasmord currently supports 41 categories in eight groups: electronics and tech; fashion and accessories; home and living; health, beauty and lifestyle; automotive; kids, baby and pets; food and consumables; and software and digital products.
Not every gap is the same problem, so the report keeps them apart:
| Finding | What it means | Typical next step |
|---|---|---|
| Missing | A fact buyers compare on is not on the page at all. | Add the fact, if you know it. |
| Conflicting | The page, a source or another listing disagree. | Decide which value is correct and fix the others. |
| Unconfirmed | A claim is stated but no matching source supports it yet. | Add a source, narrow the claim, or remove it. |
How claims are matched to sources is covered in product claims and evidence checks.
Illustrative example. Invented to show the shape of a report, not a customer result. The fuller sample layout is on the product data audit page.
Product: Example trail running shoe, model TR-2 (footwear).
| Item | Finding | Suggested change |
|---|---|---|
| Sizing | Missing — width options are not listed. | Add the widths and a sizing note. |
| Weight | Conflicting — the page says 280 g; the spec sheet says 295 g for size 9. | Confirm the value and state the size it applies to. |
| “Waterproof” | Unconfirmed — no test or membrane specification is linked. | Link the source, or soften the claim. |
| Best for | Missing — no terrain or distance it suits. | Add “best for / not ideal for”. |
| Returns | Present. | No change. |
The free check on the homepage takes one product with no signup. Plan limits for larger catalogs are on the pricing page, and the full step-by-step is on the product data audit page.
A review of a product page or catalog for missing, conflicting and unconfirmed information, so the product is easier for AI assistants and buyers to understand and compare.
Structure, Query coverage, Explainability, Comparison, Trust signals, and Freshness and availability. They are Hasmord's scoring framework for product information.
Related, not the same. An SEO audit usually targets crawling and rankings. This audit targets whether product facts are clear, comparable and supported, for AI answers and for shoppers.
No. It means your own product information is clearer on the six signals. Assistants choose products on their own, and results vary by assistant and search mode.
Missing means the fact is absent from the page. Conflicting means the page, a source or another listing disagree. Unconfirmed means a claim is stated but no matching source supports it yet.
Yes. Import products by CSV, up to 1,000 rows per file. Plan limits for larger catalogs are on the pricing page.
After material changes to the product page or its evidence, and whenever price, stock or claims drift. Checking again is part of the workflow, so you can compare reports.
Clearer product information on the six signals is one step, not the result. What matters is whether the buyer’s question was answered well enough to move them toward a purchase, and what happens next. Today you can follow AI answer checks, AI referral visits in Google Analytics 4, and the leads and opportunities in Hasmord’s CRM, each with the buyer questions behind it. The questions buyers raise in sales conversations point back to the product information: add evidence, sharpen the claim, then see how the market and sales respond.
Hasmord is the AEO-native sales platform. Buyers ask questions across AI assistants, search, and sales conversations. Hasmord uncovers where your answers about your products and services fall short, turns evidence into better answers, finds potential customers for you, and connects every lead to your sales process.
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Related: product data audit · evidence checks · product data checklist.
On Shopify? Hasmord is on the App Store as Hasmord: AI Answer Optimizer.