How to Get Recommended by ChatGPT in 2026

For two decades the goal of digital marketing was simple: rank on Google so people click your link. In 2026 a growing share of buyers never see that list of links. They ask an AI assistant a question and act on the answer it gives them. If your brand is not in that answer, you are invisible to that buyer.

This guide explains how ChatGPT (and Perplexity, and Google's AI Overviews) actually decide which brands to recommend, shows what they told us when we asked directly, and gives you a concrete playbook to become one of them. New to the topic? Start with what is GEO and then come back here.

Why getting recommended by ChatGPT matters in 2026

The shift is not hypothetical. The usage numbers are large and growing:

• ChatGPT reached 800 million weekly active users, announced by OpenAI CEO Sam Altman at the company's DevDay on October 6, 2025, and climbed to 900 million by February 2026 (TechCrunch).
• Google's AI Overviews reached 2 billion monthly users in Q2 2025, up from 1.5 billion in May 2025 (TechCrunch).
• Perplexity processed about 780 million queries in May 2025, disclosed by CEO Aravind Srinivas at Bloomberg Tech on June 5, 2025, growing more than 20% month over month (Backlinko).

The more important shift is behavioral. When an AI answer is good enough, people stop clicking. The Pew Research Center analyzed the real browsing of 900 U.S. adults and found that when a Google AI Overview appeared, users clicked through to a website just 8% of the time, compared with 15% when no overview was shown, and only 1% ever clicked a link inside the overview itself (Pew Research Center, July 22, 2025).

The takeaway: being mentioned in the answer is now its own distribution channel, separate from ranking for clicks.

How ChatGPT actually picks what to recommend

You cannot buy your way into a ChatGPT recommendation, and there is no "submit your brand" button. Instead, the model assembles an answer from the sources it considers most relevant and trustworthy, whether from its training data or from a live web search. The practical question is: what makes a source more likely to be pulled into that answer?

The most rigorous public answer comes from "GEO: Generative Engine Optimization" by Pranjal Aggarwal and colleagues, published at KDD 2024 (arXiv:2311.09735). Testing content changes against generative engines, the researchers found that targeted optimizations can boost a source's visibility in AI answers by up to 40%. The standout tactics were concrete and attributable:

• The three strongest single methods — adding relevant statistics, incorporating credible quotations, and citing reliable sources — together delivered a 30-40% relative improvement on the study's position-adjusted visibility metric.
• Citing credible sources was relatively weak on its own but became strong in combination with other methods, and helped lower-ranked pages the most.

The intuition is simple: when a model synthesizes an answer, it gravitates toward content that gives it something concrete to include, a specific number, a named expert quote, an explicit citation. Vague, generic copy gives the model nothing to lift.

We asked ChatGPT and Perplexity ourselves

To see how this plays out in our own niche, we ran five prompts a real buyer would type, in both ChatGPT (with web browsing enabled) and Perplexity, in June 2026, and recorded which brands each engine recommended and what advice it gave. The prompts were:

1. What are the best tools to track how my brand shows up in ChatGPT and Perplexity?
2. Best GEO (generative engine optimization) tools for a startup in 2026?
3. How can I tell if AI assistants recommend my company?
4. What's the cheapest way to monitor brand mentions across ChatGPT and Perplexity?
5. Otterly.ai vs Profound — which is better for AI search monitoring?

Which brands they recommended

Across the five prompts, the names that came up most often, in both engines, were:

• Otterly.ai — repeatedly named as the budget-friendly pick for small and mid-size teams.
• Peec AI — frequently cited as a strong all-round choice.
• Semrush (AI Visibility Toolkit) and Ahrefs (Brand Radar) — recommended for teams that already run serious SEO.
• SE Ranking and Sight AI — recurring mid-market mentions.
• Profound — the consistent pick for enterprise and agencies.

We publish independent reviews of two of these: Otterly.ai and Profound.

Prompt-by-prompt highlights

• Tracking your brand in ChatGPT and Perplexity: both engines led with Otterly.ai and Peec AI for smaller teams and Profound for enterprise, and emphasized features like prompt tracking, competitor share of voice, citation and source URLs, sentiment, accuracy and separate ChatGPT-vs-Perplexity reporting. Both warned that a single measurement run can be misleading.
• Best GEO tools for a startup: recommendations were staged by funding stage — Otterly.ai or Peec AI for pre-seed and seed, SE Ranking around seed-to-Series-A, Semrush or Sight AI at growth. Beyond tools, the strongest advice was strategic: don't try to "hack" ChatGPT; build authority, get cited by trusted third-party sources, publish comparison and use-case pages, then measure.
• How to tell if AI assistants recommend you: both described a real method — build a set of 30 to 100 prompts, score each answer, and record your position, competitors, the URLs cited, sentiment and accuracy. A practical tip worth stealing: check your AI referral traffic in GA4 from sources like chatgpt.com, perplexity.ai, claude.ai and gemini.
• Cheapest way to monitor mentions: the unanimous answer was to start free and manual — track your prompts in a spreadsheet — then graduate to a low-cost paid tier only once you outgrow it. Both engines also pointed to the free AI-visibility checkers from Ahrefs and Semrush.
• Otterly.ai vs Profound: both gave the same clean split — Otterly.ai for startups and SMBs that want something simple and affordable, Profound for enterprises and agencies that need deeper analytics. We reach the same conclusion in our Otterly.ai vs Profound comparison.

What both engines agreed on

Three themes recurred no matter how we phrased the question:

1. AI answers are noisy. The same prompt can return different brands on different runs, so repeated tracking beats a one-off snapshot.
2. You earn recommendations by building authority. Both engines, unprompted, advised getting cited by trusted third-party sources and publishing clear comparison and use-case content — not gaming the model. That mirrors the academic research above almost exactly.
3. Start free, then scale. Manual checks and free checkers first; paid tools when you need scale.

One finding worth stating plainly for transparency: the two tools we have affiliate relationships with, Rankscale and Orchly, did not appear in any of these AI recommendations. Both are newer entrants that have not yet built the third-party citation footprint that older names like Otterly.ai and Profound have — which is precisely the dynamic this guide is about. We mention them later on their merits for getting started cheaply, not because an AI engine named them.

The playbook: how to get recommended by ChatGPT

Notice that the engines' own advice, the academic research, and common sense all converge. Here is the practical version.

1. Be genuinely citable

This is the highest-leverage move. Whenever you publish, give models something concrete to lift: a real statistic with its source, a direct quote from a named expert, an explicit citation. Replace "our tool is fast and affordable" with a specific, attributable claim. Specific facts get quoted; vague ones get skipped.

2. Structure content for extraction

AI models reward content they can parse cleanly. Lead with a short, direct answer (a TL;DR), use a clear H2/H3 hierarchy, add a FAQ section, and mark up pages with structured data (Article, FAQPage, Product/Review). The easier it is for a machine to identify "this paragraph answers this question," the easier it is to quote you. Our guide on how to check if your brand appears in ChatGPT shows how to confirm whether your structuring is paying off.

3. Earn mentions on sources AI engines trust

This is the advice both ChatGPT and Perplexity gave us, unprompted. Models lean on sources they consider authoritative, so being mentioned, reviewed and compared on reputable third-party sites in your category increases the surface area through which an engine can encounter and cite you. Independent reviews and comparison pages are exactly the kind of corroborating sources AI answers draw on.

4. Cover the prompts buyers actually ask

Map the real questions your customers ask an assistant — the engines suggested a working set of 30 to 100 — then publish content that answers each one better than the current sources. If buyers ask "best GEO tool for a small team," there should be a clear, well-sourced page answering it, such as our best GEO tools for startups list. Phrasing matters: models are sensitive to wording, so cover several variants of the same intent.

5. Measure and iterate

You cannot improve what you do not measure, and, as both engines stressed, a single snapshot is misleading because AI answers are noisy. Start exactly where they recommended: track your prompts manually in a spreadsheet for free, and use the free AI-visibility checkers from Ahrefs and Semrush. Also watch your AI referral traffic in GA4. When manual tracking no longer scales, an affordable tool with a free or low-cost entry tier helps you run larger prompt batches across engines and track changes over time — two such options are Rankscale and Orchly; if you are weighing them, see our Rankscale vs Orchly comparison.

Where to start

Match the tool to your budget and stage:

• Free: a manual spreadsheet plus the free AI-visibility checkers from Ahrefs and Semrush — the approach both engines recommended first.
• Low-cost entry to scale your measurement: Rankscale and Orchly, both with free or low-cost tiers and quick setup.
• Budget pick named by the engines: Otterly.ai.
• Enterprise and agencies: Profound.

For a fuller ranked list, see best GEO tools for startups, or browse all of our GEO tool reviews. Whatever you choose, the workflow is the same: baseline your visibility, find the prompts where you should appear but do not, publish better content for them, and re-measure.

Sources

• OpenAI / Sam Altman, DevDay (October 6, 2025) and TechCrunch, February 27, 2026 — ChatGPT weekly active users.
• TechCrunch — Google AI Overviews monthly users (2025).
• Backlinko: Perplexity statistics — Perplexity monthly queries (May 2025).
• Pew Research Center, July 22, 2025 — click-through behavior with AI Overviews.
• Aggarwal et al., "GEO: Generative Engine Optimization," KDD 2024 (arXiv:2311.09735) — visibility impact of citations, quotations and statistics.
• Primary data: our own prompt tests in ChatGPT and Perplexity, June 2026.