How to Get Cited by Perplexity for B2B Software Queries

Perplexity shows its work. Every answer comes with numbered citations, and the API returns the search results behind them with a URL, a title and a date for each. That makes it the most legible engine in the AI Shortlist Index protocol, and the one where the age and shape of a page count for the most.

This page covers how the sonar model picks its sources for a property-ops software question and what the Index has on it so far. It ends with what a vendor should do first. The mechanism in full is in the Perplexity citation guide.

How does Perplexity pick its sources?

Perplexity’s API documentation describes the sonar models as search-first. A request runs a web search and the model writes from what was retrieved. The response returns citations, the URLs the answer rests on, and search_results, each with a URL, a title and a date.

There is no answer without retrieval on sonar. A vendor that is not in the retrieved set has no route into the answer.

The controls the retrieval layer exposes

The same documentation lists the controls a developer can set: search_recency_filter, date-range filters, search_domain_filter for allowing or blocking domains, and search_context_size. The Index protocol sets none of them, so the answer is what a property manager with nothing configured would see.

The filters still matter to a vendor, because they show what the retrieval layer knows about a page: when it was published or updated, and what domain it lives on. Perplexity’s documentation attaches a date to every search result and offers recency filtering as a first-class option. That is why we treat freshness as a stronger signal on this engine than on ChatGPT or Gemini.

Perplexity’s help center documents PerplexityBot as its crawler and says a site has to allow it for pages to surface in answers. A robots.txt rule written for a different bot two years ago can cut a vendor out of this engine entirely.

What does the Index have on Perplexity?

The September 2026 edition’s numbers come from ChatGPT; Perplexity carries no mention rate for any vendor until its first edition runs.

What the Index does have is one ChatGPT run whose consulted pages are visible: an API capture of the 300-door maintenance prompt from 3 September 2026, retired and archived. It is a useful picture of what any retrieval-first engine finds when it searches for that question.

That run consulted 124 pages: reddit.com threads (38), appfolio.com (16) and buildium.com (12), then vendor sites and a handful of listicles on buildium.com, lula.life, ustechautomations.com and essentify.ai.

Our hypothesis is that Perplexity’s citations for the same prompt draw from the same pool, weighted toward pages with a recent date. We expect the cited set to be much smaller than the consulted set, and the first edition with Perplexity runs will confirm or correct that. Sonar does not fall back on what the model remembers about your company.

What should a vendor fix, and in what order?

1. Be retrievable. Allow PerplexityBot in robots.txt, confirm your pricing, integrations and comparison pages return their text without JavaScript, and check that the canonical on each one points at itself. If the retrieval layer cannot open the page, nothing below matters.
2. Put a real date on every page that answers a buying question, and keep it true. Pricing, integrations, “for property managers with 300 doors” and “alternatives to” pages. An undated page, or one whose visible date is 2023, competes at a disadvantage against a page carrying this year’s date when Perplexity’s own filters treat recency as selectable. Update the pricing table and the integration list, then the date.
3. Shape the page so one passage answers the question. The prompt library asks “Which maintenance coordination software integrates with AppFolio?” The page that gets cited for it has a heading with AppFolio in it and a first paragraph that says, in one sentence, what the integration does.

Then the pages beyond your site

4. Be on the third-party pages a search for those queries returns. In the maintenance category that meant Reddit threads in r/PropertyManagement and r/PptyMgmtSoftware, PMS blog roundups on buildium.com, and PMS marketplace and help-center pages. The category source map is the targeting list.
5. Own the comparisons. The library asks Property Meld vs Latchel for a 600-door company, and for alternatives to Lula. A retrieval-first engine quotes whichever page answers those with a table and a date; if that page is on a competitor’s domain, the competitor wrote your entry.
6. Measure by URL, not by feel. Because the API returns the cited URL, you can check whether the citation that carried your mention was your page, a competitor’s comparison, or a listicle where you appear seventh. Those are three different positions with three different fixes.

What does the Audit find?

The Audit is $3,500 and takes seven days. It runs the full 25-prompt set for your category in ChatGPT, three runs each.

It returns your position, plainly, and the cause, as ranked findings labeled verified, inferred or hypothesis with evidence links. Then the source gap, a 90-day fix scoped to a Sprint, and what we won’t promise: rankings, traffic, leads or revenue.

Most of the fixes above carry across engines, because retrieval-first engines read the same public pages. When the Index runs Perplexity, the Audit will add every source URL per run and the date the retrieval layer saw for it. If you want to talk before requesting anything, book a 20-minute call.

The other engines have their own pages: getting your software recommended by ChatGPT, appearing in Google AI Overviews, and getting into Gemini’s grounded answers. All of it rolls up to the services page.