Product information checks
A missing recommendation does not tell you why a product was left out. Start with what you can check: accessible product information, clear specifications, relevant sources, and current commercial details. Hasmord finds the missed buying opportunities you can act on — buyer questions your product information can’t yet answer — helps you improve that information, and lets you review answers from specific AI checks.
AI assistants answer with a short list of named products, and none of them publish why one product was included and another was not. Reading a reason into the absence is guesswork. What you can work on is the information the assistant had to go on, and whether it is complete, consistent and backed by sources.
Results also move around. The same question can produce different answers on different days, in different regions and in different search modes. One answer that leaves you out is not evidence of a problem; a pattern across several checks is worth looking into.
Look at the questions, not the engine. Each request for a product recommendation is a buyer describing a need: a use, a budget, a constraint, something to rule out. When your brand never comes up, the part you can diagnose is which of those buyer questions your product information can’t answer yet. Those are missed buying opportunities, and each one points to something specific: an attribute that isn’t stated, a claim without a source for this model, a use case the page never mentions, a comparison it avoids.
Hasmord works on that side of the problem: the buyer questions and the product information behind them. It does not know why a particular assistant left your product out, and does not claim to; that reason stays unpublished.
These are the things you can confirm for yourself, roughly in the order they stop a machine from using your product facts:
Where you cannot confirm something yourself, record it as unknown rather than filling it in with a guess. See the product data checklist for how to run through these one product at a time.
Related: how to find missed buying opportunities · how Hasmord compares to other tools · ChatGPT shopping checklist · what Answer Engine Optimization means.
Being named in an AI recommendation 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.
No AI assistant publishes why a particular product was left out, so the reason cannot be read from the outside. What you can check is your own product information: whether the page can be fetched and read, whether the attributes buyers compare on are present, whether claims have sources that apply to this model, and whether price and availability are current.
No assistant publishes why one brand is named and another is not, so the internal reason stays unknown from the outside. What you can find are the missed buying opportunities: the buyer questions behind those recommendation requests that your product information can’t yet answer, such as an attribute that isn’t stated, a claim without a source for this model, a use case the page never mentions, or out-of-date price and availability. Fix those first, then compare the same questions over time; a single answer that leaves you out is not yet a pattern.
Run a product-information check on the URL. It uses Hasmord's own six-dimension framework — it is not a score assigned by ChatGPT, Google or another AI provider — and lists what is missing, conflicting or unconfirmed: the gaps that leave buyer questions unanswered. You can then run AI answer checks on realistic buyer questions and keep the answer text, with its model and date, to compare over time.
The information is yours: complete attributes, sources that match the product and model, current commercial details, and a page a machine can read. Whether an assistant names your product is its own decision, and that decision is not published.
It overlaps. SEO works on ranking a page in a list of links. Answer Engine Optimization (AEO) is the name the industry gives to preparing product information for assistants that answer a buyer directly. Hasmord works on the product information underneath it.