[ AI Product Visibility ]
Make your products easier for AI to understand
I measure how AI engines read, compare, and recommend products, categories, pages, attributes, and reviews. Where relevant, I check whether the available information is enough for a shopping agent to compare the product. Not just brand visibility: product-level visibility and readability.
E-commerce brands whose customers compare alternatives before buying
When users ask AI what to buy, you need to know whether it recommends you or a competitor.
Brands with digital catalogs
You have a catalog, product pages, and campaigns, but do not know whether the available information is sufficient to distinguish and compare products.
E-commerce teams with SEO in place
Ranking and traffic are not enough if AI recommendations cite marketplaces or alternative products.
Sound familiar? Send me the context and I'll tell you whether starting here makes sense.
Analyze your catalog- 01
Product query map
I map commercial prompts by category, use case, price, comparison, problem, alternative, and buying intent.
- 02
Recommendation audit
I check whether ChatGPT, Perplexity, Gemini, Claude, and Google AI recommend your products or your competitors' products.
- 03
Product pages and catalog readiness
I review product pages, categories, schema, merchant data, attributes, variants, images, reviews, and FAQ content.
- 04
Priority content gaps
I define what is missing to make products easier to understand: buying guides, comparison pages, attributes, tables, and verifiable claims.
- 05
Operating roadmap
I deliver a sequence of interventions for product pages, categories, and source content, prioritized by commercial impact.
[ First step required ]
Selection of 20-100 representative SKUs and access to catalog, feed, product pages, and available performance data. The sample exists to find the systemic problems, which are almost always few and repeated.
[ after the baseline ]
A catalog changes with every collection, so the measurement has to be repeated. Quarterly re-measurement on the same question set is a separate retainer, attached to the baseline.
AI Visibility Monitor[ advertising on ChatGPT ]
For eligible catalogs, a separate pilot can test ChatGPT Ads and product feeds without attributing organic effects to the campaign.
Explore ChatGPT Ads Pilot- I do not touch platform code. I deliver operational specifications; whoever owns the storefront applies them.
- I do not start from the whole catalog. One cycle works on the sample and produces the specification for fixing the rest, and in that order the work actually finishes.
- I do not fill attributes with generic values to complete the schema. A badly populated attribute produces a wrong comparison instead of a missing figure.
- I do not run campaigns or advertising feeds inside this service. The paid channel, where eligible, has a separate pilot.
[ the next step ]
A catalog audit leaves a list of interventions that repeats with every new collection, every price change, every new product. Fixing a hundred pages by hand once is a project; keeping them consistent over time is a process, and a process gets designed.
Want to know where to start?
Send context, website, and objective. I'll reply with the most sensible first step.