Track and Win Claude With Answer Radar

What the engine records, and why the panel is narrow

For each approved question, Answer Radar asks Claude once through its official web search tool, with no steering. It stores the full answer, whether Claude searched, the search queries it issued, every source Claude attached to a sentence of its answer, flagged as yours, a competitor's or a third party's, and every brand named with whether it was recommended or only listed.

Claude's citations are attached to the text it writes, not to the list of pages it searched. Answer Radar records only those attached sources, so a URL that merely appears in the prose is not a citation. That is why the panel is narrow: 3.93 sources per answer against Perplexity's 9.8. A commenter in r/content_marketing, in the thread 'What strategies are actually working to get your brand mentioned in ChatGPT, Gemini, Claude, and Perplexity responses?', described the same behaviour: 'Claude is conservative about citing anything that is not established.'

Claude decides when to search. When it does, the row carries a Searched pill and is graded grounded. When it does not, the row has no pill, is graded ungrounded, and is reported separately with the reason not searched; nothing you publish changes that answer until the model changes.

Running the first audit that includes Claude

Nothing extra to enable. Approve the buying questions, press Run audit, check the estimate such as '12 approved questions across 5 answer engines', and press Confirm & run.

From inside Claude, connect the Linkeddit connector and ask it to estimate and start the audit. The connector exposes answer_audit_estimate, which spends nothing, and answer_audit_start, which reserves the credits and fans the questions out to all five engines.

Reading the Claude rows

Open a run from History. Under the heading Claude, every question has a row with a Searched pill when Claude searched and one of three chips: You: recommended, You: mentioned or You: not mentioned. Expand it to see Queries, the searches Claude ran, then Recommended followed by the brands put forward, or No brand recommended, then the answer and a Citations table with the columns Domain and Who.

Two or three rows in the Citations table is normal for this engine, and each one is worth more than a slot in a panel of ten. In measured customer answers the most cited domains were crm.org, superbcrew.com, sybill.ai, bestcrmforstartups.com and pipedrive.com, small editorial and vendor sites rather than large review platforms.

A commenter in r/SEO, in the thread 'How do you get your site recommended by ChatGPT and Claude?', advised recording 'the full citation URL, not the domain, since which page type gets pulled is most of the information.' Answer Radar stores the full URL for every citation.

From a gap to a saved and verified fix, inside Claude

Start with grounded rows where a competitor sits under Recommended. Ask Claude, through the connector, to list the gaps for the run with answer_gaps_list and open one with answer_gap_get. The gap carries the answer text, the cited sources and the requirement your page failed.

Drafting is Claude's job, not ours. Have Claude draft the page or the outreach from the evidence, then save it with answer_fix_save so it sits in the project's evidence store and action queue. When the change is live, start a verification with answer_verify_start and read the result with answer_verify_get; the re-measurement reports the observed change, labelled as observed.

Leave ungrounded rows alone. Claude answered from memory, there is no cited page to change, and a fix spent there measures nothing.

Where the numbers appear

In the app, the project overview and each run page show presence and recommendation for Claude over grounded answers, with the ungrounded count beside them. Through the connector, answer_metrics_get and answer_trends_get return the Claude segment with its disclosure block, which names the sample counts and grades behind every number. The weekly brief lists Claude on its own line.

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