Solutions / B2B SaaS founders

Find out whether AI assistants recommend your product, then fix the page that decides it

Most of our users are founders, most of them solo, most of them in B2B SaaS. This is the version of Answer Radar built for that person: ten approved questions, one run, a ranked list of where the engines chose someone else, and a re-measurement you do not have to remember to do.

Start with the Compete plan How Answer Radar works

Updated 2026-09-04

Who this is for, from our own onboarding data

Read-only query on our database, 4 September 2026. Denominators included, because a percentage without one is a claim.

Of 321 people who told us their role, 203 said founder, which is 63%. Of 310 who named an industry, 119 said B2B SaaS, 38%, ahead of consumer apps at 39 people and agencies at 29. Of 317 who gave a team size, 225 were solo, 71%, and another 52 were teams of two to five. The plan we wrote for this page had guessed 48% B2B SaaS; the table says 38%, so 38% is what is printed. The product decisions on this page follow from that person: one seat, no analyst, an assistant already open.

Our own baseline, run on 4 September 2026

This is what a founder sees after the first run: presence and recommendation per engine with the denominator, who leads share of voice, and whether your own domain was cited. Ours is not flattering, which is why it is here.

answer_metrics_get({
  project_id: "<your project id>",
  run_id: "19910a90-4d5d-4549-8b7f-1116184f9b5a"
})

Through the Linkeddit MCP connector, or the project's Overview in the app. 21 prompts, one sample each, four engines.

Presence and recommendation, linkeddit.com project

Claude: Named in 2 of 10 admissible answers, recommended in 0 of 10. Share of voice leaders: Semrush 5.8%, Ahrefs 4.8%, Crayon 3.8%. Citations: linkeddit.com 2 of 35 citing samples.
Gemini: Named in 3 of 15 admissible answers, recommended in 1 of 15. Share of voice leaders: Semrush 8.3%, then Ahrefs, Crayon, Klue, Peec at 4.5%. Citations: linkeddit.com 3 of 105; reddit.com led at 5.
ChatGPT: Named in 0 of 7 admissible answers, recommended in 0 of 7. Share of voice leaders: Crayon and Semrush 8.1% each. Citations: linkeddit.com absent; g2.com 2 of 33.
Perplexity: Named in 2 of 3 admissible answers, recommended in 0 of 3. Share of voice leaders: Five vendors tied at 8.3% on three samples. Citations: linkeddit.com 2 of 29.

Admissible samples only: Claude excluded 5 of 15 as ungrounded or thin, ChatGPT 9 of 16, Perplexity 10 of 13. A segment with no admissible samples reports usable false and no metrics, never zero. Every metric carries its definition and version (visibility-metrics-v1).

The founder's week, in four steps

Written for one seat and no analyst. Each step is a tool call if you would rather not open the app.

Step 1

Propose, then approve ten to twenty questions

Unbranded, commercial intent: best tools for the job, alternatives to the competitor you lose to, the pricing question. Our own set has fifteen of these at the top and a handful of Linkeddit-named ones at the bottom.

Step 2

Run once, read the gaps

One audit across four engines. The gap list ranks the questions where an engine recommended someone else or nobody, by intent. Ours had nine open on the first run.

Step 3

Fix the one page the evidence points at

The content gap view names the page that was closest. Draft against the recorded evidence, check the sources, publish. Your assistant writes the words; the guard refuses sources the engines never returned.

Step 4

Schedule the re-measurement and stop looking

Set the project to weekly or monthly, register the verification, and read the brief when it arrives. It says plainly when nothing moved, which for a solo founder is the most useful sentence in the product.

What the engines said about our own category

Same run. The competitor intelligence and AI visibility categories, as four engines see them, with our own brand mostly absent.

Engine	Named	Recommended	Share of voice leaders	Who got cited
Claude	2 of 10	0 of 10	Semrush 5.8%, Ahrefs 4.8%, Crayon 3.8%	linkeddit.com 2 of 35 citing samples
Gemini	3 of 15	1 of 15	Semrush 8.3%, then Ahrefs, Crayon, Klue, Peec at 4.5%	linkeddit.com 3 of 105; reddit.com led at 5
ChatGPT	0 of 7	0 of 7	Crayon and Semrush 8.1% each	linkeddit.com absent; g2.com 2 of 33
Perplexity	2 of 3	0 of 3	Five vendors tied at 8.3% on three samples	linkeddit.com 2 of 29

Semrush and Ahrefs lead because the engines read listicles and review profiles that name them, not because either measures AI answers particularly well. That is the founder's lesson in one row: the pages the engine reads decide the answer, and the content gap view tells you which of yours was supposed to be one of them.

What we will not tell a founder

Because a small team cannot afford to act on a number that was made up.

How much traffic ChatGPT sent you. The engines pass little or no referrer, so any such figure is inference. We report what is observable: named, recommended, cited.
How often people ask an assistant your question. No engine publishes it, so no prompt volume is printed and proposals are not ranked by an invented one.
That your fix caused the movement. A re-measurement reports an observed delta with a threshold and control prompts. There is no field for a cause.
That a score went up. Share of voice and mean position are not subtracted between runs; presence and recommendation rate are, on identical denominators.

Frequently asked questions

Does AEO matter for a B2B SaaS company?+

It matters exactly as much as your buyers ask an assistant before they shortlist. You can measure that instead of guessing: run your category's buying questions across ChatGPT, Gemini, Perplexity and Claude and see whether you are named, recommended or absent. On our own project the answer in September 2026 was mostly absent, and that number, not a trend piece, is what decides whether to spend time on it.

What is the easiest way for a founder with no team to know if AI assistants recommend my product?+

Approve ten questions, run one audit, read the gap list. That is under an hour, and the result is a ranked list of the questions where an engine recommended a competitor or nobody, with the sources it cited. If you work in Claude or Cursor, the same three steps are tool calls in the connector and you never open the app.

Who actually uses Linkeddit?+

Of the 321 people who told us their role during onboarding, 63% said founder. Of 310 who named an industry, 38% said B2B SaaS, the largest group by a wide margin, followed by consumer apps at 13% and agencies at 9%. Of 317 who gave a team size, 71% were solo. Read-only query on our own database, 4 September 2026. The product is shaped around that person.

How is this different from the AI visibility trackers built for enterprise?+

Three ways that matter at founder scale. The measurement carries the fix and the re-measurement, so you are not buying a dashboard and then a consultant. The whole thing is callable over MCP, so a one-person team can run it from the assistant they already use. And Answer Radar is included in the Compete plan alongside the weekly competitor brief, rather than priced as a separate line.

What did your own numbers look like?+

On 4 September 2026, across 21 prompts and four engines: presence 2 of 10 on Claude, 3 of 15 on Gemini, 0 of 7 on ChatGPT, 2 of 3 on Perplexity; recommended once, on Gemini. Semrush led share of voice on three of four engines. We publish that because it is the honest baseline a founder starts from, and because the re-measurement will be published too.

What does it cost?+

Answer Radar is included with the Compete plan at $99 a month, with no per-engine or per-prompt charge. Runs draw on a monthly credit allowance, one credit per question asked of one engine, and the usage tool tells you the position before you start.

Ten questions, one run, and a list of where you are missing

Answer Radar is included with the Compete plan, alongside the weekly competitor brief, in the browser and through the MCP connector.

See pricing Run it from your assistant