Guides · Updated October 18, 2026

How to Get Your Brand Recommended by Perplexity

Perplexity is the easiest AI assistant to reverse-engineer, because it shows its work. Every answer is built from a live web search and comes with numbered citations. If Perplexity doesn’t recommend you, the sources under the answer usually show you why.

So the plan is short: make sure Perplexity can read your site, find out which pages it cites for your buyers’ questions, get named on those pages, and make your own pages easy to quote. Then keep measuring.

How Perplexity builds an answer

Perplexity describes itself as an answer engine. When you ask a question, it searches the internet in real time, gathers material from the sources it finds, writes a summary, and attaches numbered citations that link to those sources (Perplexity Help Center). Pro users can choose which model writes the answer, but the sources still come from Perplexity’s own search.

Perplexity has also described how that search works (Perplexity engineering blog):

• It retrieves by keywords and by meaning. It combines lexical and semantic retrieval, so a page can be found whether it uses the buyer’s exact words or just answers the same question.
• It filters out stale content early. Out-of-date or clearly irrelevant results are removed before ranking.
• It scores passages, not just pages. Results are ranked at the document and sub-document level, so the model gets the most relevant chunk of a page rather than the whole thing.

Two things follow from that. First, unlike ChatGPT, which can answer from memory, Perplexity’s recommendations depend on what its search turns up for that question today. Second, a single clear passage can win you a citation, even on a page that’s about something broader.

Perplexity doesn’t publish how it decides which retrieved sources make it into an answer, or which brands it names. Everything below follows from what it does document plus what you can see in the citations.

Step 1: See what Perplexity says about you today

• Write 5–10 questions a real buyer would ask before buying in your category, without your brand name in them: “best X for Y,” “alternatives to [category leader],” “X vs Y,” and one about a specific use case.
• Ask each one in a new thread, so earlier questions don’t steer the answer. Repeat each question two or three times. Answers vary from run to run.
• For every answer, record: were you named, in what position, how you were described, which competitors were named, and every cited URL.

The cited URLs are the valuable part. After 10–20 answers you’ll usually see the same domains coming back: a few “best X tools” roundups, a review site, a Reddit thread, a competitor’s comparison page. That list is your map for Steps 3 and 4.

Step 2: Let Perplexity’s crawlers in

Perplexity runs two user agents, and they do different jobs (Perplexity crawler docs):

• PerplexityBot crawls the web to surface and link sites in Perplexity’s search results. Perplexity says it isn’t used to crawl content for AI foundation models, and recommends allowing it in robots.txt if you want to appear in search results.
• Perplexity-User visits a page when a user’s question needs it. Because a person requested the fetch, Perplexity says it generally ignores robots.txt.

What to check:

1. robots.txt. Open yourdomain.com/robots.txt and make sure nothing disallows PerplexityBot, either by name or through a blanket User-agent: * rule. Perplexity says changes can take up to 24 hours to register. If a page is blocked, Perplexity says it won’t index the page’s text, though it may still index the domain, headline and a brief factual summary (Perplexity Help Center). That isn’t enough for it to quote you.
2. Your firewall or CDN. Bot protection can block PerplexityBot even when robots.txt allows it. Perplexity publishes its IP ranges and recommends allow rules that match both the user agent and those IPs, with step-by-step notes for Cloudflare and AWS WAF (Perplexity crawler docs).
3. Plain HTML. Your one-line description, who it’s for, pricing and key features should be readable text, not only rendered by JavaScript or baked into images.

Blocking OpenAI’s GPTBot or other AI crawlers doesn’t affect Perplexity, since each one is a separate robots.txt rule. Check that a “block AI bots” setting at your host hasn’t swept up PerplexityBot by accident.

Step 3: Get named on the pages Perplexity already cites

This is usually where the biggest gains are. Go back to your list of cited URLs from Step 1 and mark the ones that name competitors but not you.

• Roundups and “best X” articles: email the author with facts they can check: what you do differently, who it’s for, current pricing, and a free account to test. An update to an article Perplexity already cites is worth more than a new article it never retrieves.
• Review and listing sites that show up in your citations (G2, Capterra, Product Hunt, app stores, niche directories): complete your listing and ask real customers for detailed reviews. Never write or buy reviews.
• Community threads (Reddit, forums, Indie Hackers): answer honestly under your own name and say you built the product. Undisclosed promotion breaks most communities’ rules.
• Wrong or outdated descriptions: if a cited page has old pricing or a discontinued feature, ask the publisher to fix it. Perplexity can only repeat what the page says.

Step 4: Write passages Perplexity can quote

Because Perplexity ranks passages, not just pages, structure your own pages so the right passage is easy to find and safe to quote.

• Use the buyer’s question as a heading, then answer it in the first sentence or two below. “Is [Product] good for small teams?” followed by a direct, specific answer.
• Make each section stand on its own. A passage pulled out of context should still say what the product is and who it’s for.
• Publish honest comparison pages (“[You] vs [Competitor]”) that say plainly when the other product is the better fit. For “X vs Y” questions, a fair page on your own site is often the best source available.
• Keep key pages current and dated. Perplexity filters out stale results early, so an updated pricing or features page with a visible date has a better chance than one that hasn’t changed in years.
• Add evidence, not keywords. In the GEO study from Princeton and IIT Delhi researchers (KDD 2024), adding citations, quotations and statistics to a source made it more visible in generative engine answers, while keyword stuffing did little (Aggarwal et al.). The authors also ran the methods on Perplexity itself, supplying the source pages as uploaded files, and saw similar gains. It’s a research setup, not a promise, but the direction is consistent: specific, sourced content gets quoted.

Step 5: Measure every week or month

Re-run the same questions on a schedule and compare:

• The share of answers that name you
• Your position when you’re named, and whether the description is accurate
• Which competitors show up
• Which domains Perplexity cites, and whether any of your target pages have started citing you

Look for changes that hold across several questions and runs, not one answer. If you also track ChatGPT, Gemini and Claude, expect different results: each searches and ranks differently, so a page that wins on Perplexity may not show up elsewhere. Our guide on getting recommended by ChatGPT covers the differences on that side.

Common mistakes

• Blocking PerplexityBot by accident with a firewall rule or a blanket “block AI bots” switch.
• Testing with your brand name in the question. “Is [Brand] good?” tells you nothing about whether Perplexity recommends you to someone who has never heard of you.
• Ignoring the citations. They’re the closest thing to a to-do list any AI assistant gives you.
• Letting key pages go stale. Old pricing on your own site or a cited roundup gets repeated back to buyers.
• Astroturfing Reddit or review sites. It gets removed, and it can cost you your listings.

FAQ

Sources

• Perplexity, “Perplexity Crawlers”
• Perplexity Help Center, “How does Perplexity follow robots.txt?”
• Perplexity Help Center, “How does Perplexity work?”
• Perplexity, “Architecting and Evaluating an AI-First Search API”
• Aggarwal et al., “GEO: Generative Engine Optimization,” KDD 2024