Ranko continuously scans ChatGPT, Claude, Perplexity, and Gemini to find the real questions people are asking AI assistants about your topics not the questions a keyword tool guesses they might be asking. You get the actual phrasings, refreshed daily for the hot topics and weekly for everything else, so the team always knows exactly what the answer engines are being asked about your space. An always on listening layer for the half of search that no traditional research tool can see.
Tell Ranko the topics that matter to your brand. The platform begins continuously scanning ChatGPT, Claude, Perplexity, and Gemini for the actual questions being asked about those topics. The real phrasings get captured as they are asked, with hot topics refreshed daily and the rest refreshed weekly, so the team always sees what the answer engines are being asked about your space right now.
Topic Setup
Pick the topics your brand actually cares about the product categories, the competitor names, the customer problems, the industry concepts. Mark which ones are hot actively being launched, recently in the news, central to a current campaign and which ones are evergreen long tail. Ranko sets up continuous listening across all four AI assistants for every topic on the list, with the cadence tuned to how much movement the team needs to track.
Four Engine Scan
Ranko runs continuous scans across ChatGPT, Claude, Perplexity, and Gemini for every topic you have set up. The four engines do not ask the same questions in the same way Perplexity users tend to ask longer research style queries, ChatGPT users tend to ask conversational follow up style queries, Claude users tend to ask careful nuanced questions, Gemini users tend to ask transactional comparison style questions. All four patterns get captured separately, so the team can see how each engine's audience genuinely approaches the topic.
Real Phrasing Capture
The questions arrive in your dashboard exactly as real people are phrasing them every "how do I", every "what is the best", every "is it worth it", every awkward conversational turn, every weirdly specific edge case. No clean keyword versions, no standardised search shapes, no synthetic rewrites. The team finally sees the actual language their audience uses when they talk to an AI assistant, which is the language the team's content has to match if it wants to be the answer the AI cites back.
Refresh Cadence
Hot topics get refreshed every day so the team catches the shifts as they happen a competitor's launch changing how people ask about the category, a news event reshaping the questions around a product, a feature release pulling in a new wave of curiosity. Evergreen topics get refreshed weekly, which is the right cadence for slower moving questions that drift gradually rather than sharply. The team sees what changed and what stayed the same since the last refresh, so the signal is always current without ever overwhelming.
Once a team can see the real questions being asked about their topics across all four major AI assistants captured in the exact phrasings real people use and refreshed at the right cadence the old pattern of optimising for the questions you think people ask stops being acceptable. These are the changes that show up first.
"How do I compare SOC 2 and ISO 27001 for an early stage SaaS" is a different question than "SOC 2 vs ISO 27001". Keyword tools collapse both into one stale phrase. Ranko captures the full conversational shape, which means the team's content can match the way people actually ask and the AI assistant cites the content that matches its user's question word for word over the content that only matches in concept.
Treating all AI assistants as interchangeable is how teams end up optimising for the wrong audience. Ranko watches ChatGPT, Claude, Perplexity, and Gemini separately because their user behaviour is genuinely different. Perplexity asks research questions. ChatGPT gets conversational follow ups. Claude attracts nuanced specifics. Gemini sees comparison flows. The team sees the texture of each engine's audience, which lets the strategy adapt to where the brand wants to win.
Traditional question research is a snapshot you take once and forget about until quarter end. Continuous mining means the team has a living view of how the conversation is moving across every topic that matters new questions appearing, old questions fading, phrasings shifting as the language of the category evolves. The "we did our keyword research six months ago" excuse that quietly costs traffic month after month simply does not apply.
When a competitor launches a new feature, when a regulation changes, when a news cycle catches your category, the questions people ask the AI assistants shift in hours, not weeks. Daily refresh on hot topics catches the shift the day it happens, so the team can publish the timely answer while the timing still matters not three weeks later when somebody else has already become the cited source for the new wave of questions.
Evergreen topics move slowly the questions around them drift over months rather than days. Weekly refresh is the right cadence for that pace: fresh enough to catch genuine shifts, calm enough not to drown the team in noise from week to week variation. The compounding library of citable content for slower moving topics builds steadily on a foundation that stays accurate without constant attention.
The captured questions do not just sit in a dashboard. They flow directly into Topic Planner so future plans are built on real demand, into Content Forge so future articles answer the actual questions, into Analytics Forge so the team can prove the work led to citations. The listening layer becomes the foundation the rest of the engine runs on, which means continuous question mining quietly improves every other part of the platform too.
"How do I compare SOC 2 and ISO 27001 for an early stage SaaS" is a different question than "SOC 2 vs ISO 27001". Keyword tools collapse both into one stale phrase. Ranko captures the full conversational shape, which means the team's content can match the way people actually ask and the AI assistant cites the content that matches its user's question word for word over the content that only matches in concept.
Treating all AI assistants as interchangeable is how teams end up optimising for the wrong audience. Ranko watches ChatGPT, Claude, Perplexity, and Gemini separately because their user behaviour is genuinely different. Perplexity asks research questions. ChatGPT gets conversational follow ups. Claude attracts nuanced specifics. Gemini sees comparison flows. The team sees the texture of each engine's audience, which lets the strategy adapt to where the brand wants to win.
Traditional question research is a snapshot you take once and forget about until quarter end. Continuous mining means the team has a living view of how the conversation is moving across every topic that matters new questions appearing, old questions fading, phrasings shifting as the language of the category evolves. The "we did our keyword research six months ago" excuse that quietly costs traffic month after month simply does not apply.
When a competitor launches a new feature, when a regulation changes, when a news cycle catches your category, the questions people ask the AI assistants shift in hours, not weeks. Daily refresh on hot topics catches the shift the day it happens, so the team can publish the timely answer while the timing still matters not three weeks later when somebody else has already become the cited source for the new wave of questions.
Evergreen topics move slowly the questions around them drift over months rather than days. Weekly refresh is the right cadence for that pace: fresh enough to catch genuine shifts, calm enough not to drown the team in noise from week to week variation. The compounding library of citable content for slower moving topics builds steadily on a foundation that stays accurate without constant attention.
The captured questions do not just sit in a dashboard. They flow directly into Topic Planner so future plans are built on real demand, into Content Forge so future articles answer the actual questions, into Analytics Forge so the team can prove the work led to citations. The listening layer becomes the foundation the rest of the engine runs on, which means continuous question mining quietly improves every other part of the platform too.
Continuous scans across ChatGPT, Claude, Perplexity, and Gemini. Real phrasings captured exactly as asked. Daily refresh for hot topics. Weekly refresh for the rest. The listening layer your content engine has always needed.
8800+
Teams listening to what AI assistants are actually being asked
Founders watching how their category is being talked about in the answer engines, content marketers shaping briefs from the exact phrasings their audience actually uses, search specialists adjusting to a world where AI assistants split the traffic with Google, agencies running listening posts across multiple client topics from one cockpit, growth teams at SaaS companies tracking how competitor mentions shift in the AI engines, ecommerce operators watching how shoppers describe products before they buy, and PR teams catching the questions that follow a launch or a news cycle all use Ranko's question mining as the always on listening layer for the half of search that no other tool can see. Every team a small business tracking a handful of topics or a larger organisation watching hundreds of topic surfaces across every product line gets the same continuous four engine coverage and the same real phrasing capture.
Engines
Hot
Rest
Phrasings
Ranko runs continuous scans across ChatGPT, Claude, Perplexity, and Gemini for every topic on your watchlist. The four engines have genuinely different user behaviour different question shapes, different conversational patterns, different audience needs and Ranko captures each one separately so the team sees the real texture of the half of search that no other tool can see.
A complete continuous question discovery toolkit built into the same answer engine optimisation platform your team already uses. ChatGPT mining, Claude mining, Perplexity mining, Gemini mining, real phrasing capture, and the daily and weekly refresh cadence come together so the team always knows exactly what the answer engines are being asked about every topic that matters.
Continuous capture of the conversational follow up style questions that define ChatGPT user behaviour the iterative "and what about" probes, the clarifying "in that case" pivots, the long form requests that build on previous answers. The team sees how its audience actually talks to ChatGPT, which is the conversation its content has to match if it wants to be the answer ChatGPT quotes back.
Continuous capture of the careful nuanced questions that define Claude user behaviour the considered "what are the trade offs" framings, the specific edge case explorations, the questions that ask Claude to weigh competing concerns. Claude's audience tends to ask deeper questions, and the team finally sees that depth in the exact language its readers use when they want a thoughtful answer.
Continuous capture of the longer research style queries that define Perplexity user behaviour the questions that read like a brief, the multi part asks, the requests for citations and comparison tables. Perplexity's audience is the most research minded of the four engines, and the team that wants to be the cited source there has to match that exacting tone.
Continuous capture of the transactional comparison style questions that define Gemini user behaviour the "which is best for", the "should I choose A or B", the price and feature shaped queries that often sit closer to commercial intent. Gemini's audience tends to convert sooner, and the team finally sees the buying language that wins citations on that engine.
Every question arrives exactly as real people are phrasing it conversational tone, awkward turns, weirdly specific edge cases, everything intact. No standardised rewrites, no clean keyword versions, no synthetic templates. The team sees the actual language its audience uses, which is the language its content has to match to earn the citation.
Hot topics refresh every day so the team catches shifts the day they happen. Evergreen topics refresh weekly, which is the right cadence for slower moving questions. Change detection flags exactly what moved between refreshes, so the signal stays current without ever drowning the team in week to week noise from topics that have not really shifted.
Continuous capture of the conversational follow up style questions that define ChatGPT user behaviour the iterative "and what about" probes, the clarifying "in that case" pivots, the long form requests that build on previous answers. The team sees how its audience actually talks to ChatGPT, which is the conversation its content has to match if it wants to be the answer ChatGPT quotes back.
Continuous capture of the careful nuanced questions that define Claude user behaviour the considered "what are the trade offs" framings, the specific edge case explorations, the questions that ask Claude to weigh competing concerns. Claude's audience tends to ask deeper questions, and the team finally sees that depth in the exact language its readers use when they want a thoughtful answer.
Continuous capture of the longer research style queries that define Perplexity user behaviour the questions that read like a brief, the multi part asks, the requests for citations and comparison tables. Perplexity's audience is the most research minded of the four engines, and the team that wants to be the cited source there has to match that exacting tone.
Continuous capture of the transactional comparison style questions that define Gemini user behaviour the "which is best for", the "should I choose A or B", the price and feature shaped queries that often sit closer to commercial intent. Gemini's audience tends to convert sooner, and the team finally sees the buying language that wins citations on that engine.
Every question arrives exactly as real people are phrasing it conversational tone, awkward turns, weirdly specific edge cases, everything intact. No standardised rewrites, no clean keyword versions, no synthetic templates. The team sees the actual language its audience uses, which is the language its content has to match to earn the citation.
Hot topics refresh every day so the team catches shifts the day they happen. Evergreen topics refresh weekly, which is the right cadence for slower moving questions. Change detection flags exactly what moved between refreshes, so the signal stays current without ever drowning the team in week to week noise from topics that have not really shifted.
Common questions about how the continuous mining actually works, what counts as a real phrasing, how Ranko decides between daily and weekly cadence, how the team marks a topic as hot, what happens when a brand new question appears, and how this differs from the Topic Planner.
Ranko runs ongoing scans against ChatGPT, Claude, Perplexity, and Gemini for every topic on your watchlist, capturing the questions real people are asking around those topics through licensed signal sources, observed prompt patterns, and the publicly surfaced question patterns each engine exposes. The scans run continuously in the background the team does not start a job, kick off a run, or refresh anything manually. New questions arrive in the dashboard as they are mined, and existing questions get updated as the language around them shifts over time.
Continuous scans across ChatGPT, Claude, Perplexity, and Gemini. Real phrasings captured exactly as asked. Daily refresh for hot topics. Weekly refresh for the rest. The always on listening layer your content engine has always deserved.