SpyFu holds an advertising and keyword archive reaching back to 2007. AnswerRank records which competitors AI assistants name today, on the questions your buyers ask, along with the sources each engine cites to justify the recommendation.
SpyFu's data describes a marketplace. Positions are bought, spend is inferred, ad copy is archived, and a competitor's bidding pattern reveals their intent. For paid search that model holds exactly, and the depth of the archive is the reason to buy the product.
An answer engine has no auction. The model retrieves a handful of sources, weighs what it holds from training, and names two or three brands in prose. There is no cost-per-click to compare, no ad rank to reverse-engineer, and no lower slot for a diligent competitor to occupy. A brand is either in the answer or it is absent.
Competitive intelligence still applies; the unit of measurement is different. Instead of tracking spend, you track how often an answer names a rival, on which questions, and which sources the engine leaned on when deciding. That last part matters most, because the sources are the surfaces where the position was actually won.
There is no bid to raise and no slot to buy inside an answer. Engines name the brands they can corroborate, from what they retrieved and what they absorbed in training.
Review platforms carry disproportionate weight. G2, Capterra and Trustpilot pages are structured, updated and independent, which makes them ideal corroboration for a model deciding between vendors. A competitor with a current review profile and recent responses is materially easier for an engine to recommend than one with a stale page.
Community threads come next. Reddit, Stack Exchange and category forums read as first-hand experience, and engines lean on them when a buyer asks what actually works rather than what exists. These threads are also durable, since a well-answered question keeps being retrieved for years.
Independent comparison articles complete the set. A roundup on a trade publication is a single page that can place a brand into hundreds of answers, which is why source requests from journalists are worth answering promptly. Owned documentation still matters, mostly for accuracy: it is where an engine checks specifics like pricing, limits and integrations once a brand is already in consideration.
The share of answers in a category naming a given brand. The answer-engine equivalent of share of voice, measured by parsing generated answers rather than by estimating spend.
A buyer question where a competitor is named and you are absent, or where no brand holds the answer at all. The second kind is the cheaper target.
A page an engine quotes when justifying a claim. Collected across runs, these reveal the surfaces that decide recommendations in a category.
The live search an engine performs while composing an answer. Retrieval-driven visibility responds within weeks, unlike visibility carried in model weights.
A crawler such as ChatGPT-User or Perplexity-User that opens a page while a user waits. Blocking one prevents an assistant from reading your page on request.
Answer engine optimization and generative engine optimization, two names for making a brand the one an assistant recommends.
A standing table of which brands the assistants name in your category, how often each appears, and on which questions. Movement is the earliest available signal that a rival's content or PR programme is working, arriving before it shows in their traffic.
The pages each engine quoted, collected per answer and ranked by frequency across runs. Over weeks this identifies the review profiles, forum threads and comparison articles that actually decide recommendations in your category.
Live requests from journalists and podcast hosts, scored against what your brand knows, each with a deadline, a publication and a route to reply. These expire, which is precisely why no archive contains them. Answering one places you in an article before it is written.
The community threads engines already cite in your category, with the subreddit, the question and the thread age. Participation goes where the answer is being sourced instead of where activity merely looks high.
Query competitor mindshare, citations and gaps in plain language from Claude, Cursor or another MCP client. Useful while drafting a battlecard or a board update, where the figures are needed in context rather than as an export.
The same buyer questions run on a schedule across ChatGPT, Claude, Perplexity, Gemini and Google AI answers, recording your position against named rivals each time. Engines change behaviour without announcing it, and a tracked series is how that becomes visible.
Sales teams meet AI answers before marketing hears about them. A buyer arrives having already asked an assistant which tools to consider, carrying a shortlist and a set of assumptions taken from whatever the model said. Knowing the content of that answer changes the first call.
The mindshare table supplies the shortlist. If assistants consistently name three competitors alongside you on the questions your buyers ask, those three are in the room whether or not the buyer mentions them, and the battlecard should cover exactly those and not the ones a rep remembers from last year.
The citation list supplies the claims. When an engine recommends a competitor it is usually paraphrasing a specific review, comparison article or forum thread, and reading that source shows the exact framing a prospect has absorbed. Correcting a misconception is far easier when you know the sentence it came from.
Question gaps supply the openings. Buyer questions where no brand holds the answer are the topics where a prospect has no prior view, which makes them the strongest ground for a discovery call and the clearest brief for the next piece of content. Pulling all three through MCP into whatever tool a rep already uses keeps the update to a sentence rather than a report.
Add the two or three brands that appear in your deals. Every scheduled run then records their presence beside yours, which turns a single snapshot into a time series you can act on.
Split the question set into those a rival consistently owns and those no brand has claimed. The open questions convert faster, since winning them requires publishing a better answer rather than displacing an established one.
When a competitor is named repeatedly, the citation list usually explains why. A review profile, a much-quoted forum thread or one comparison article tends to be doing the work, and that page is the target rather than their homepage.
Get onto the pages the engines already quote. Update review profiles, answer the threads that keep being retrieved, and reply to source requests in your subject area. Each is a durable placement rather than a campaign.
A blocked answer-time fetcher removes you from consideration regardless of everything above. Confirm OAI-SearchBot, PerplexityBot and ChatGPT-User are allowed before attributing a loss to a competitor's strength.
Read each engine separately. Retrieval-led surfaces respond within weeks while model-weight visibility moves on training cycles, so a rival gaining on Perplexity and holding steady on Claude is a normal pattern with a specific cause.
Crawler configuration accounts for the largest share. A robots.txt written from a template that predates answer-time fetchers frequently blocks OAI-SearchBot or ChatGPT-User while leaving the training crawlers open, which is the reverse of what most teams intend. The effect is absolute, since a page that cannot be fetched cannot be quoted.
Thin third-party presence accounts for much of the rest. A brand with a strong site, an outdated G2 profile and no presence in the forum threads its category discusses gives an engine nothing independent to cite. Competitors with weaker products and better corroboration are named instead, and the ad spend data shows nothing unusual because none of this involves advertising.
Vague positioning causes quiet losses. Engines answer questions like which tool suits a specific situation, and a brand whose material never states plainly what it is for and who it is not for cannot be matched to a situation. Competitors with narrower, more explicit claims get named on the specific questions where the buyer intent is clearest.
Stale pages complete the list. Retrieval favours current material, so pricing pages with old figures, comparison pages naming discontinued competitors and undated guides are all passed over for fresher sources. Each of these four has a specific fix, and the citation list usually indicates which one applies before any content is rewritten.
SpyFu figures are the listed monthly prices, checked August 2026. Annual billing reduces them by roughly a quarter.
SpyFu wins paid search research. An archive of competitor keywords, bids and ad copy reaching back to 2007 cannot be reconstructed by a newer product, and for anyone planning or auditing a paid programme that history is the entire value. Its organic ranking data serves the same purpose for classic SEO competitive work.
AnswerRank wins the recommendation. A rival can be absent from the auction entirely and still be the brand every assistant names, because engines read their documentation, their reviews and the threads discussing them. None of that activity leaves a trace in advertising data, and it is the whole of what this product measures.
That is the central report. Every collected answer is parsed for the brands it names, and those names accumulate into a mindshare table for your category showing who is named, how often, and on which buyer questions. It is the answer-engine counterpart to a competitor keyword report.
Most competitor intelligence tools report on paid and organic search, since that is where the data has existed. AnswerRank collects competitor presence inside AI answers instead, tracking who gets named across five engines. The two data sets barely overlap, so running both is common for teams doing paid search alongside AEO.
SpyFu lists Basic at $39 a month, Pro with AI features at $119 and Team at $249, with roughly 25% off annual billing, checked August 2026. AnswerRank is $29 a month with unlimited seats and every feature included. The plans are not equivalent, since SpyFu sells an advertising archive reaching back to 2007 and AnswerRank sells continuous measurement of AI answers.
SpyFu's core data is Google paid and organic search intelligence, with AI-branded features on its higher tier. Its model assumes an auction and a ranking table. Answer engines have neither, so tracking presence inside a generated answer requires collecting the answers themselves.
Question gap analysis. Instead of phrases a rival ranks for and you do not, you get buyer questions where a rival is named in the answer and you are absent, plus questions where no brand has become the standard answer. The second group usually moves fastest, since there is no incumbent to displace.
There is a REST API, a Zapier integration and an MCP server. MCP lets Claude, Cursor or another client return competitor mindshare, citations, content gaps and link opportunities in plain language, which suits battlecards and board updates better than a CSV.
Add competitor brands during setup and every scheduled run records their presence alongside yours. The mindshare table then becomes a time series, so a rival gaining share on comparison questions is visible weeks before it appears in their traffic or their ad spend.
Weekly suits most categories. Retrieval-led engines can change within days when a new source appears, and reading the same question set on a fixed cadence separates a genuine shift from ordinary sampling variance. Daily tracking mostly adds noise, since model behaviour rarely changes meaningfully inside twenty-four hours.
Yes. Competitors are named per project, so an agency can run separate projects for separate clients and a team entering a new market can track the incumbents there before launching. Each project keeps its own question set, its own competitors and its own history.
Third-party corroboration moves it most. Review platforms, forum threads and independent comparison articles are the sources engines cite when justifying a recommendation, and a competitor who is named consistently is usually well represented there. Owned pages matter for accuracy and completeness rather than for persuasion.
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