Answer Radar · AI Brand Visibility & Answer Engine Optimization
Answer Radar is an AI brand visibility and monitoring tool. It tracks your brand across ChatGPT, Gemini, Perplexity and Claude: which buying questions name you, which name a competitor, and every source the answer cites. Then it turns each gap into a source-backed fix and re-measures - so you can win the answer, not just the search result.
A buyer asks
“What's the best tool for competitor and demand intelligence?”
The answer recommends
A short loop you can run end to end: set up, measure, fix, re-measure.
01
Add your domain and competitors, and approve the commercial-intent buying questions your customers actually ask. Answer Radar drafts the questions for you; you keep the ones that matter.
02
Answer Radar puts those questions to AI answer engines and captures the response: who gets recommended, which sources are cited, and whether your brand shows up at all.
03
Gaps are ranked by intent and evidence. Each comes with the observed sources and a drafted fix. Ship it, then re-measure to see the answer change - no guarantees, just measurement.
Observed evidence and clearly-labeled hypotheses, kept separate - so every recommendation you act on is grounded in what the answer engines actually said.
See, question by question, where AI assistants send buyers - and whether you're in the answer or on the sidelines.
Track which competitors get recommended for your category's buying questions, and where you're being left out.
Every source an answer cites is captured, so you can see exactly which pages are shaping what the AI says.
Gaps are ranked by commercial intent and evidence strength - observed facts kept separate from hypotheses.
Turn a gap into a drafted brief, article, or response, grounded only in evidence from the run - never invented.
Run the whole flow in the browser, or connect the Linkeddit MCP to Claude and other AI assistants - no local setup, no copied secret.
Answer Radar sits alongside competitor intelligence and demand intelligence - one view of where buyers are looking, what they ask, and who the answers point them to.
An AI brand visibility tool measures whether AI assistants name your brand when someone asks them which product to buy. It puts real buying questions to engines like ChatGPT, Gemini, Perplexity and Claude, records who gets recommended and which sources the answer cites, and tracks how that changes over time. Answer Radar does this and adds the step most tools stop short of: it turns each miss into a source-backed fix and re-measures afterwards.
Pick the commercial-intent questions your buyers actually ask, run them against every engine on a schedule, and record three things per run: whether you appear, who is recommended instead, and which sources are cited. Answer Radar runs GPT, Gemini, Perplexity and Claude from one project so the results are comparable across engines, and keeps the observed answer text so a score can always be traced back to evidence.
Yes. Answer Radar records every brand named in each answer, not just yours, so you can see which competitor owns which buying question and which sources the engine leaned on to get there. That competitor view is the point: a visibility score alone does not tell you what to change, but knowing a rival is being cited from a specific source does.
Most AI visibility tools stop at measurement: they report a score and leave you to guess the fix. Answer Radar runs the full loop - measure, rank the gaps by intent and evidence, draft a fix grounded only in the sources observed in that run, then re-measure the same question to see whether the answer moved. It also keeps observed facts strictly separate from hypotheses, and reports a failed run as a failure rather than a false zero.
Answer Radar is included with Linkeddit's Compete plan at $99 per month, which also covers the weekly competitor intelligence brief. It is built for founders and small teams rather than enterprise procurement, so there is no seat minimum and no annual commitment.
Answer engine optimization is making sure AI answer engines recommend and cite you when buyers ask which product to use. Traditional SEO optimizes for a ranked list of blue links; AEO is about being present - and recommended - inside the AI's answer itself. Answer Radar measures that and shows you where you stand.
SEO gets your page to rank; AEO gets your product into the answer an AI gives a buyer. They overlap - cited sources matter for both - but the unit of measurement is different. Answer Radar tracks whether you're recommended and cited for real buying questions, not just whether a page ranks.
Answer Radar measures GPT, Gemini, Perplexity, and Claude today. It captures the answer text, who gets recommended, and every cited source, and it normalizes failures honestly rather than reporting a false zero.
No. You can run the entire flow - set up a project, generate and approve questions, review gaps, and draft fixes - in the browser. If you work in AI assistants, you can also connect the Linkeddit MCP connector to Claude and others: one URL, sign in once, no local server and no copied secret.
Answer Radar is included with the Compete plan. See the pricing page for current details.
The research behind this page: why answer engines name who they name, what the tools in this category actually cost, and how to measure any of it honestly.
Set up a project, approve the buying questions, and see who the answer engines recommend - and where you fit in.
Free, no account
Check one prompt against a real answer engine, generate a full prompt set, and ship a valid llms.txt file today. Answer Radar is what tracks the set on a schedule and shows whether any of it moved.
Check whether AI assistants recommend your product for a real buyer question, and see which competitors they name instead.
Generate the buyer prompts AI assistants actually get asked about your category, grouped by funnel stage.
Build a valid llms.txt file for your site, including the citation and safety blocks most generators leave out.