Answer Radar · Answer Engine Optimization
Buyers increasingly ask AI assistants which product to pick. Answer Radar measures where those answer engines recommend your competitors instead of you, 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.
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 starts with the leading answer engine using grounded web search, and coverage is rolling out to more engines. It captures the answer text, who gets recommended, and every cited source, and it normalizes failures honestly rather than reporting a false zero. We add engines as they meet our evidence-quality bar.
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.
Set up a project, approve the buying questions, and see who the answer engines recommend - and where you fit in.