AI Visibility Audit: What 15 SaaS Buying Questions Revealed

1What did our AI visibility audit find?

AI visibility auditing means checking whether a brand appears in answers to a defined set of buying questions, then inspecting the sources and competitors in those answers. Our saved Answer Radar summary records Linkeddit as absent from nine of fifteen questions about competitive intelligence and AI visibility.

For a SaaS founder or growth team, the immediate question is which omission matters to a real buying decision. A broad category term, a request for a price-change alert, and a question about sources cited by ChatGPT can represent different needs. We used this baseline to make those distinctions in our content.

2What was measured, and what is missing?

The measurement date is September 4, 2026. This article uses an existing internal Answer Radar summary for our own domain, now transcribed into a public summary of the recorded counts. It is a review of that saved measurement, not a new run. Linkeddit sells Answer Radar, so this is first-party product evidence.

The available summary does not include raw answers, model versions, exact full prompt wording, or a breakdown of the six questions where Linkeddit appeared. It cannot establish what a particular person would see in a consumer chat interface. The appendix contains the nine missed-question labels and domain counts that are available, rather than claiming to be the full run export.

Mentions, citations and recommendations need separate metrics. The reported six-question presence count combines mentions and citations; it is not a recommendation rate, win rate or share of voice. This one-day, one-category sample also cannot establish a market-wide pattern or explain Google rankings.

3Which observations were useful for our SaaS positioning?

1. Comparison sources deserve investigation

For the recorded B2B SaaS competitive-intelligence question, saashero.net had six citation occurrences, meertrack.com three and klue.com two. That makes those sources candidates for inspection. Domain counts alone do not identify the exact cited page, explain why it was chosen or prove that inclusion on it would produce a recommendation.

2. “AI citation tracking” needs a definition

The citation-tracking question included scite.ai twice, alongside mendeley.com and help.openalex.org, as well as marketing-oriented domains. This mixture prompted us to define our intended task explicitly: tracking sources cited in AI buying answers. We should not assume an ambiguous phrase means brand visibility to every reader or engine.

3. Pricing monitoring is its own buying task

The pricing-and-launches question recorded three citation occurrences each for price2spy.com, thunderbit.com and visualping.io. Our interpretation is that a reader may want change detection before a broader intelligence platform. Our pricing guide therefore needs to explain SaaS plan, seat and usage comparisons, and when the next step is a decision brief.

These are hypotheses informed by the recorded sources. They do not prove how an engine understood a prompt or which content change will work.

4What decisions did this investigation help us make?

These are editorial decisions, not a customer success claim. The revised SaaS pricing-intelligence workflow and buying-question guide give a reader a concrete next step. We have not measured a post-change improvement.

5Which nine questions did Linkeddit miss?

These labels and domain counts are transcribed from the saved summary. An unnumbered domain represents one citation occurrence. They are sources cited in answers, not a ranked list of recommended products.

6How should a small SaaS team repeat this workflow?

1. Choose questions tied to software selection, a competitor comparison or a task your product actually supports. Keep exploratory prompts separate from a stable comparison set.
2. Record prompt text, date, engine/model, response, source URLs, failures and retries. Score mentions, recommendations and citations separately.
3. Inspect the missed answers and their sources. Select a gap that your product can honestly address; do not rewrite your positioning around every source that appears.
4. Document the source observation, proposed interpretation, page change and expected buyer action. Preserve the original baseline.
5. Repeat the stable question set after publication and indexing time, recording any engine changes. Compare Google queries separately in Search Console and assess qualified product activity independently.

If you work in an assistant, the Claude Code competitive-intelligence workflow explains how to bring recorded evidence into the drafting process through MCP.

Frequently asked questions