What Software Tracks If Ai Engines Recommend My Product

• Dedicated AI visibility trackers are tools that run a fixed set of buyer prompts against ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews, then record what each engine says.
• A single visibility score hides the detail you need.
• Three surfaces get grouped under the same label, but they are measured differently.
• Pricing varies widely and is usually metered by prompts, projects, clients or engines.
• Results are volatile by design.

Dedicated AI visibility trackers are tools that run a fixed set of buyer prompts against ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews, then record what each engine says. For example, according to Friction AI, Friction AI tracks prompts across ChatGPT, Claude, Gemini, Perplexity and Google AI Overviews.

It separates being mentioned from being actively recommended, and that distinction matters. A brand can appear in a list of options while the answer steers the buyer toward someone else. Shopping-focused tools go further. According to Finseo, Finseo checks whether products are recommended by ChatGPT, Google AI Mode and Perplexity, including the price shown and the competitors named.

Fastlook's Citation Analytics takes a similar approach for brand and category prompts, tracking where your brand appears across major engines with real-time reporting. Before choosing a tool, confirm which engines it covers and whether it records recommendations or only mentions.

A single visibility score hides the detail you need. Tracking should measure mention rate, which is how often the brand appears. It should also measure recommendation position, showing whether the brand is first, second or buried. Share of voice against named competitors matters, as do the citation sources the engine used and the sentiment of the answer. MaxAEO's buyer's guide makes the same case, arguing that tracking should measure these components rather than rely on one number. Shopping tools add product-level detail. According to CrunchJunkie, its shopping recommendation rate is the share of purchase-intent runs in which AI recommends a brand. That rate is reported with a sample size and a plus-or-minus margin of error. CrunchJunkie also flags low confidence when a result rests on fewer than ten runs. Ask any vendor for the same three things:

• The run count
• The margin of error
• The list of prompts used

Three surfaces get grouped under the same label, but they are measured differently. The first is a general assistant's text answer, such as a ChatGPT reply to 'best project management tool for agencies'. The second is a shopping feature's product cards, such as ChatGPT Shopping, Perplexity Shopping or Google AI Mode. The third is a marketplace assistant, such as Amazon's Rufus, now Alexa for Shopping.

According to Greater Than Services, these are separate surfaces tracked by different tools. Traditional rank trackers answer a different question entirely: where a URL sits in a fixed list of results for a keyword. An AI answer has no fixed list, so rank position does not transfer. Choose the tool by the surface your buyers actually use.

Pricing varies widely and is usually metered by prompts, projects, clients or engines. Otterly AI's pricing page lists a Standard plan at $189 for 100 prompts and a Premium plan at $489 for 400 prompts. Peec AI's agency pricing publishes monthly tiers of $245, $495 and $795 covering 3, 10 and 25 client projects. Trakkr's agency guide reports AthenaHQ agency bands below 5, at 5 or more, and at 20 or more brands, plus a $295 Starter plan with 3,600 monthly credits. Ethan Smith, CEO of Graphite, argued on Lenny's Podcast that some tools charge hundreds or thousands of dollars a month for basic keyword-style tracking, according to Position Digital's review. Agency portfolio prices and white-label controls often need direct confirmation from the vendor.

Prices are as published by each source at the time of research and may have changed.

Results are volatile by design. According to Position Digital's review, SparkToro's experiment found less than a 1% chance that ChatGPT and Google's AI will return the same list of brands in two separate answers. A single run therefore tells you almost nothing. Read trends instead:

• Run the same prompts repeatedly
• Compare the averages over time
• Check the margin of error

Treat a shift of a few points inside a small sample as noise. Watch for consistency in the sources the engine cites, since those citations reveal what the model trusts. Brand Memory, which builds a structured source of truth from your site and tracks citations across six engines, is designed around this same idea: stable facts that engines can repeatedly read and cite.

Start with where the purchase happens. If you sell on your own site, prioritise general assistant answers and brand-level citation tracking. If you sell on a marketplace, prioritise the marketplace assistant and product-level data, because Amazon's assistant is a separate surface from ChatGPT. If you run AEO for several clients, prioritise multi-project pricing and reporting, since per-prompt plans get expensive across accounts. Greater Than Services observes that tools differ by surface, so a single tool rarely covers all three well.

Fastlook's Page Engine can publish AEO-optimised pages to WordPress, Webflow or Shopify, which closes the loop between finding a gap in recommendations and fixing the content behind it. Confirm each vendor's engine coverage, sample sizes and white-label options before committing.