Tag: Perplexity

ChatGPT Owns AI Referrals: What 6.77M Sessions Show

A year ago, I watched the industry place its bets on which AI platform would own discovery. Perplexity looked like the search-native challenger. Copilot looked like the enterprise Trojan horse. In the data I’m seeing now, neither bet has really paid off.

Previsible (disclosure: I’m its CPO and co-founder) just published its third AI Traffic Study, based on 6.77 million LLM-driven sessions. What stands out to me is the level of consolidation. Monthly LLM sessions grew 9.9x, reaching 644,478 in May 2026, and 92.4% of that traffic came from one platform.

The plateau was a pause

In mid-2025, it looked like AI traffic might be topping out in some sectors. I don’t think that was the real story.

Sessions climbed from 65,249 in November 2024 to 396,278 by August 2025. Then they dropped sharply in November 2025 before reaching new highs of 428,203 in February 2026 and 644,478 in May.

That November dip deserves context.

Sessions fell 50% in a single month, driven almost entirely by ChatGPT referrals dropping from 448,412 to 213,345. Other platforms were mostly steady. To me, that points to a model-related change. We’ve already seen small product shifts create major swings in referral traffic, including last fall, when many sites lost half their ChatGPT traffic because the model began favoring Wikipedia and Reddit. By December, sessions had recovered to 442,609.

The lesson I take from this is simple: one vendor’s product decision can cut your AI traffic in half overnight. I would plan for that volatility instead of treating AI referrals as a stable channel.

Consolidation, not competition

When we last published in December 2025, ChatGPT held about 84% share. Perplexity followed at 8.9%, Gemini at 4.5%, Copilot at 2.1%, and Claude at 0.6%. Six months later, the field had moved even more decisively toward the leader.

Across the full dataset, ChatGPT now commands 92.4% of trackable LLM referral traffic. It grew 12.8x over 19 months, with no clear sign of slowing. It is the only LLM sending meaningful referral volume at scale, which means I would not talk about “AI visibility” without putting ChatGPT first.

There is one important caveat. This study measures standalone LLM referral traffic. AI discovery inside Google’s own results, including AI Overviews, almost certainly drives more AI traffic than all standalone platforms combined. But that operates under a different measurement model, so it is not included here.

The challengers flipped

The surprise is not that ChatGPT is on top. What I find more interesting is the movement beneath it.

Claude

Claude grew 64x, moving from 133 sessions in November 2024 to 8,528 in May 2026. It overtook Perplexity in March 2026 for the first time, and it stayed ahead.

Claude was mostly flat through 2025, then accelerated 4x in two months as its agentic tools and enterprise integrations gained adoption. The enterprise advantage many people expected Copilot to win may be materializing for Claude instead.

If your audience includes technical buyers, developers, or professional services, I would treat Claude visibility as material now. The early positioning window is still open, but it may not stay that way for long.

Gemini

Gemini is the quiet number two in this dataset. It delivered 3.2x growth with very little volatility. Because Gemini is tied into Workspace and Android, I suspect referral numbers undercount its real discovery footprint.

Perplexity & Copilot

Perplexity peaked at 17,507 monthly sessions in March 2025 and has fallen 61% since. Copilot fell even harder, dropping 96% from its August 2025 peak, from 8,651 sessions to 339.

I no longer see either platform as a strong traffic-acquisition growth bet. Both are shifting toward experiences that keep users inside their own environments, including browsers, agents, and modes where they do not need to send traffic out at all.

Where LLMs send users, and why it should change your roadmap

The most actionable finding in the study is not market share. It is where LLMs send people after they decide a site is worth visiting.

ChatGPT sends 28.8% of its traffic to internal search results pages. Across industries, roughly 25% of AI-referred traffic lands on internal search.

Old search marketing tools give way to a faster, connected future, with data streams, AI icons, and a glowing search hub symbolizing SEO innovation and community growth.

My read is that the model trusts the domain but cannot always identify the exact right page. So it sends users to the site’s search box and lets them navigate from there. Because this pattern holds across verticals and time periods, I see it as structural to retrieval-augmented generation rather than a temporary quirk.

That changes the role of internal search. The model already did the hard work of choosing your domain. Now your internal search experience decides whether that high-intent visit converts or bounces.

For most sites, internal search is still treated like a neglected navigation feature. I think it needs to be treated as an acquisition surface.

The vertical-level data tells several different stories. SaaS traffic lands on search pages 34.6% of the time. Publisher traffic lands on news pages 54% of the time, but against 120+ million organic sessions, publisher penetration is only 0.11%. Publishers create the content LLMs cite, yet they capture almost none of the resulting traffic.

Ecommerce traffic tends to land on product pages, often with purchase intent already formed. Education traffic lands directly on course pages 52% of the time, bypassing marketing content. Health traffic lands on About pages 42.1% of the time, suggesting users are evaluating the source before trusting the content. Legal traffic spreads across blog, about, contact, and location pages, which reflects the full evaluation arc.

The platforms have distinct behaviors, too. ChatGPT and Gemini act more like search-pattern models: they show domain trust but page-level uncertainty. Perplexity and Claude behave more like content-selection models, picking specific pages and over-indexing on long-form content.

If your strategy depends on editorial content driving qualified traffic, I would give Perplexity and Claude more attention than their raw share suggests.

What I would do now

First, I would optimize for ChatGPT before anything else and expand to other platforms only when the volume justifies the work. ChatGPT is where the measurable standalone LLM referral traffic is concentrated.

Second, I would monitor Claude closely. It overtook Perplexity in March 2026, and early visibility advantages can compound quickly when a platform is still forming its citation and recommendation patterns.

Third, I would treat product pages as AI entry points. Product pages capture 43% of ecommerce LLM traffic, which makes structured, comparable product data a discoverability requirement rather than a nice-to-have.

Fourth, I would make pricing machine-readable wherever possible. “Contact us for pricing” gives AI systems very little to summarize, compare, or recommend.

Fifth, I would prioritize internal search. It is not just a navigation feature anymore. For AI-referred users, it may be the first real conversion point.

Finally, I would track AI traffic by page type instead of relying only on site-wide averages. Your overall AI traffic number can hide where the real concentration is. A pricing page, for example, might run 3x your site-wide penetration.

The next question I want answered is conversion rate by LLM platform. Which platforms send users who buy, and which send users who bounce?

We built this dataset to answer that. If the last 19 months are any guide, I expect the answers to change faster than most teams are prepared for.

About the data

This analysis includes 166 GA4 properties from November 2024 through May 2026, spanning SaaS, ecommerce, finance, legal, health, insurance, education, publishing, and ticketing. All 166 properties are present throughout the full 19-month window, so I’m looking at behavioral change rather than sample expansion.

The report

You can find the full report at previsible.io.

Inspired by this post on Search Engine Land.

July 6, 2026

The 6 Best Agentic SEO Companies to Watch in 2026

Agentic SEO is the newest branch of search optimization, and I see it as one of the most important shifts marketers need to understand now. Instead of focusing only on traditional search rankings, agentic SEO is about earning visibility, trust, and conversions inside agentic search platforms such as ChatGPT Agent and Claude CoWork. Many marketers expect it to become a major acquisition channel by 2027.

To identify the top agentic SEO companies, I evaluated 38 firms in Q2 2026 and scored each one across five weighted factors.

AI Visibility Score (30%): How often an agency’s clients appear in AI citations.
SEO, GEO & ASO Expertise (25%): The depth of an agency’s approach to SEO (Search Engine Optimization), GEO (Generative Engine Optimization), and ASO (Agentic Search Optimization).
Notable Clients (20%): The recognizable brands an agency has worked with.
Average Review Score (15%): Direct client satisfaction feedback.
Leadership Experience Score (10%): How long senior staff have worked in the search optimization industry.

After reviewing the data, I found six companies that stood out from the rest. Below, I break down each agency’s strengths, specialty, scores, and client review themes.

The Top Agentic SEO Companies of 2026

Rank	Agency	AI Visibility Score	SEO, GEO, & ASO Expertise	Notable Clients	Average Review Score	Leadership Experience Score	Specialty
1	First Page Sage	4.9	5.0	Salesforce, Logitech, US Bank	4.9	4.8	Agentic SEO & GEO
2	Genevate	4.6	4.8	ZipRecruiter, CBRE, Talentfoot	4.8	4.2	GEO-First Search Optimization
3	Driven Metrics	4.4	4.4	AutoStar Transport Express, Dignity Health, Affirmed Home Care	4.7	4.3	Results-focused SMB SEO with GEO capabilities
4	Seer Interactive	4.2	4.8	LinkedIn, Intuit, Capital One, Autodesk	4.2	4.8	Enterprise-scale SEO and analytics
5	Omniscient Digital	4.2	4.5	Hotjar, Smartling, Loom	4.8	4.2	Content-Led Organic Growth
6	Go Fish Digital	3.9	4.5	Jelly Belly, Ruffwear, Joybird	5.0	4.5	Technical GEO & Citations

First Page Sage

I ranked First Page Sage first because it is the only firm on this list with a dedicated agentic SEO practice. Its work is built on a commercial GEO methodology the firm has been running since 2023. I also like that its offering connects SEO, GEO, and Agentic SEO into one integrated program, so clients do not need separate teams managing traditional organic search and agentic AI visibility.

First Page Sage’s approach focuses heavily on content, thought leadership, on-site authority, and independent third-party coverage. These are the kinds of signals AI models often draw from when evaluating whether a brand should be cited, recommended, or included in a comparison. Its Agentic SEO and GEO work also helps clarify how major models position a client at the comparison stage, including content that explains who a product is for and reduces the ambiguity that can cause AI agents to skip a vendor.

In one campaign, a skincare brand’s AI sentiment score rose 4 points across tracked models in roughly 14 weeks, and agents recommended the brand more often across the board. I see this approach as especially strong for considered-purchase categories such as B2B software, financial services, and healthcare, where AI endorsement can influence which vendors make a buyer’s shortlist.

AI Visibility Score: 4.9
SEO, GEO & ASO Expertise: 5.0
Notable Clients: Salesforce, Logitech, US Bank
Average Review Score: 4.9
Leadership Experience Score: 4.8
Specialty: Agentic Search Optimization & GEO
Contact: First Page Sage

Summary of online reviews: In client feedback, First Page Sage’s Agentic SEO work is described as “incredibly innovative and well-executed,” with reviewers saying the agency has “become a game-changing part of [their] marketing strategy.” Several clients also say the team “helped [them] gain a first mover advantage within [their] industry” and describe staff as “extremely detail-oriented and communicative.”

Genevate

I ranked Genevate second because it is one of the few firms built specifically for the generative-search era. Genevate focuses on the external signals large language models pull from, including media placements, authoritative third-party mentions, and a consistent brand narrative across the web. By combining GEO with digital PR, the firm helps shape how ChatGPT, Perplexity, and Claude describe and recommend a brand to prospective buyers.

The main limitation I found is Genevate’s shorter track record. Founded in 2025, it has one of the briefest operating histories on this list, and its scope is intentionally narrow. The firm focuses on GEO and reputation management rather than broader agentic search or traditional SEO. For brands that already have media momentum and want tighter control over their AI narrative, that focus can be an advantage. For companies that need a wider service mix, I would weigh that limitation carefully.

AI Visibility Score: 4.6
SEO, GEO & ASO Expertise: 4.8
Notable Clients: ZipRecruiter, CBRE, Talentfoot
Average Review Score: 4.8
Leadership Experience Score: 4.2
Specialty: GEO-First Search Optimization
Contact: Genevate

Summary of online reviews: Early clients describe Genevate as a “responsive partner” with a strong grasp of “how AI platforms describe and recommend brands,” and they speak positively about the firm’s “PR-led approach.” Teams looking for a full-service marketing partner “may find the offering is best paired with separate performance marketing.”

Driven Metrics

I see Driven Metrics as a strong fit for small and mid-sized businesses that want disciplined SEO and GEO without the cost structure of a larger agency. Its methodology starts with buyer intent, mapping each keyword to a funnel stage instead of chasing search volume alone. From there, the firm produces content across landing pages, pillar articles, guides, FAQs, and other formats while also handling the technical work needed to keep a site indexable and ready for generative AI systems.

Because Driven Metrics is a younger and smaller firm, I would place it in a different category than the larger enterprise agencies on this list. Its GEO work appears credible and structured, but its operating history is shorter and its client base leans more mid-market than enterprise. For growth-stage companies that want a practical SEO and GEO foundation, it is a natural starting point.

AI Visibility Score: 4.4
SEO, GEO & ASO Expertise: 4.4
Notable Clients: AutoStar Transport Express, Dignity Health, Affirmed Home Care
Average Review Score: 4.7
Leadership Experience Score: 4.3
Specialty: Results-focused SMB SEO with GEO capabilities
Contact: Driven Metrics

Summary of online reviews: Clients appreciate Driven Metrics’ “clear, well-constructed process” and describe the team as “communicative and easy to work with.” Reviewers also note that the agency has “a limited track record and few documented wins to point to,” and that buyers from larger firms “may find the experience underwhelming.”

Seer Interactive

I ranked Seer Interactive highly because it brings more than two decades of SEO experience into AI search. Data is the center of its model. The firm runs large-scale analyses to identify where clients should focus, then builds content and technical improvements around what the numbers show. Seer has also packaged its GEO offering as a defined product and has published AI search experiments since 2023, with work cited by Search Engine Land, Semrush, and Ahrefs.

On the technical side, Seer is especially useful for enterprise-scale problems that smaller agencies may struggle to handle. That includes large site architectures, technical markup at scale, and measurement systems that track how a brand appears in AI answers across thousands of pages.

The tradeoff is that Seer’s breadth can dilute specialization. Most of its case studies focus on traffic and conversions rather than pipeline or revenue, and the full-service model spreads senior attention across many client needs. I would consider Seer a strong fit for larger organizations that want AI search handled alongside a broader SEO program, but not necessarily for buyers who want a dedicated agentic specialist above all else.

AI Visibility Score: 4.2
SEO, GEO & ASO Expertise: 4.8
Notable Clients: LinkedIn, Intuit, Capital One, Autodesk
Average Review Score: 4.2
Leadership Experience Score: 4.8
Specialty: Enterprise-scale SEO and analytics
Contact: Seer Interactive

Summary of online reviews: Clients credit Seer with “deep analytical horsepower and senior, experienced teams,” and they value its “measurement-first approach.” The process is also described as “thorough, but slow,” and “lean teams wanting quick turnarounds will feel it.”

Omniscient Digital

I view Omniscient Digital as the content-led choice on this list. The agency builds GEO around editorial quality, original expertise, and the brand and author authority signals AI models consider when selecting sources to cite. Its work also includes digital PR, machine-readable markup, and tracking across major LLMs as part of each engagement.

The downside is that this kind of editorial depth takes time. Results may build more gradually than they would with a narrow technical fix. Engagements also start at around $10,000 per month, with no self-serve option, which puts Omniscient out of reach for many earlier-stage teams. The firm also shares relatively little about its GEO methodology publicly, so buyers have limited visibility into exactly how citations are tracked and improved.

For well-funded B2B software companies that want to own the language of their category, I think Omniscient can be a strong option. For teams with tighter budgets or urgent timelines, the ramp-up period and pricing are important considerations.

AI Visibility Score: 4.2
SEO, GEO & ASO Expertise: 4.5
Notable Clients: Hotjar, Smartling, Loom
Average Review Score: 4.8
Leadership Experience Score: 4.2
Specialty: Content-Led Organic Growth
Contact: Omniscient Digital

Summary of online reviews: Clients praise Omniscient for “editorial-quality writing and thoughtful, well-scoped strategy.” Some also note that the work is “slow to ramp.”

Go Fish Digital

I included Go Fish Digital because it brings a long technical SEO history into the GEO conversation. The agency built its GEO practice around Barracuda, a proprietary AI platform that scores pages against the signals AI systems use to evaluate and cite sources. Go Fish maps how AI interprets a site’s coverage, then improves machine-readable markup, authority signals, and fact density to make a brand more likely to appear in AI answers.

The biggest limitation I found is the lack of published GEO-specific results. That is common in such a new field, but it still matters when comparing providers. I would consider Go Fish Digital a good fit for established brands that want a technical, platform-backed AI search approach from an agency with a longer operating history.

AI Visibility Score: 3.9
SEO, GEO & ASO Expertise: 4.5
Notable Clients: Jelly Belly, Ruffwear, Joybird
Average Review Score: 5.0
Leadership Experience Score: 4.5
Specialty: Technical GEO & Citations
Contact: Go Fish Digital

Summary of online reviews: Reviewers consistently praise Go Fish Digital’s “full-package capability” across technical SEO, content, and PR, and call the team “responsive and well-organized.” The clearest gap is GEO proof: “the process is clearly defined, but public results in AI search are still thin,” which reflects how new the practice remains.

The Top Agentic AI Optimization Companies by Specialty

I also compared these leading agentic AI optimization companies by buyer segment, since the right agency depends heavily on company size, budget, and search maturity.

Top 5 for Enterprise Organizations

First Page Sage
Genevate
Seer Interactive
Omniscient Digital
Go Fish Digital

Top 5 for B2B Software & SaaS Companies

Seer Interactive
First Page Sage
Genevate
Driven Metrics
Omniscient Digital

Top 5 for Growth-Stage & Mid-Market Businesses

First Page Sage
Driven Metrics
Genevate
Omniscient Digital
Go Fish Digital

Source

Inspired by this post on First Page Sage Blog.

July 3, 2026

Does llms.txt Matter for AI SEO? What My Data Shows
I have watched the debate around llms.txt become one of the most polarized conversations in web optimization.

Some people treat llms.txt as essential infrastructure for AI discovery. Others, especially longtime SEO practitioners, see it as speculative theater. Platform tools are starting to flag missing llms.txt files as site issues, yet server logs still show that AI crawlers rarely request them.

Google even appeared to adopt it. Sort of. In December, Google added llms.txt files across many developer and documentation sites.

At first, the signal looked obvious to me: if the company behind the sitemap standard was implementing llms.txt, maybe the file really mattered.

Then Google removed it from its Search developer docs within 24 hours.

Google’s John Mueller said the change came from a sitewide CMS update that many content teams didn’t realize was happening. When asked why the files still exist on other Google properties, Mueller said they aren’t “findable by default because they’re not at the top-level” and “it’s safe to assume they’re there for other purposes,” not discovery.

The llms.txt research

I wanted data, not another debate.

So I tracked llms.txt adoption across 10 sites in finance, B2B SaaS, ecommerce, insurance, and pet care. I looked at the 90 days before implementation and the 90 days after.

I measured AI crawl frequency, traffic from ChatGPT, Claude, Perplexity, and Gemini, and the other changes each site made during the same window.

Here is what I found:
- Two of the 10 sites saw AI traffic increases of 12.5% and 25%, but llms.txt was not the cause.
- Eight sites saw no measurable change.
- One site declined by 19.7%.
The 2 ‘success’ stories weren’t about the file

The Neobank: 25% growth

One digital banking platform implemented llms.txt early in Q3 2025. Ninety days later, its AI traffic was up 25%.

That sounds compelling until I looked at what else happened during the same period.
- The company ran a PR campaign around its banking license and earned coverage in major national publications.
- It restructured product pages with extractable comparison tables for interest rates, fees, and minimums.
- It published 12 new FAQ pages optimized for extraction.
- It rebuilt its resource center with new banking information and concepts.
- It fixed technical SEO issues, including header structure problems.
When a company earns Bloomberg coverage in the same month it launches optimized content and fixes crawl errors, I cannot isolate llms.txt as the growth driver.

The B2B SaaS platform: 12.5% growth

A workflow automation company saw AI traffic jump 12.5% two weeks after implementing llms.txt.

The timing looked perfect. It would be easy to call the case closed. But the surrounding context told a different story.

Three weeks earlier, the company had published 27 downloadable AI templates covering project management frameworks, financial models, and workflow planners. These were functional tools, not ordinary content marketing assets, and they drove the engagement behind the spike.

Google organic traffic to those templates rose 18% during the same period and kept climbing throughout the 90 days I measured.

Search engines and AI models surfaced the templates because they solved real problems and created an entirely new site section. They did not surface them simply because the URLs appeared in an llms.txt file.

The 8 sites where nothing happened after uploading llms.txt

Eight sites saw no measurable change after adding llms.txt. One of them declined by 19.7%.

The decline came from an insurance site that implemented llms.txt in early September. Based on the data, the drop likely had nothing to do with the file.

The same pattern appeared across all traffic channels. Llms.txt did not prevent the decline, and it did not create any visible advantage.

The other seven sites, which included ecommerce brands in pet supplies, home goods, and fashion, plus B2B SaaS, finance, and pet care sites, used llms.txt to document their best existing content. That content included product pages, case studies, API docs, and buying guides.

Ninety days later, nothing changed. Traffic stayed flat. Crawl frequency was identical. The content was already indexed and discoverable, and the file did not change that.

The pattern was clear: sites that launched new, functional content saw gains. Sites that only documented existing content saw no gains.

Why the disconnect?

No major LLM provider has officially committed to parsing llms.txt. Not OpenAI. Not Anthropic. Not Google. Not Meta.

Google’s Mueller put it plainly:
- “None of the AI services have said they’re using llms.txt, and you can tell when you look at your server logs that they don’t even check for it.”
That is the reality I saw in the data. The file exists. The advocacy exists. But platform adoption does not show meaningful use yet.

The token efficiency argument and its limits

The strongest case for llms.txt is efficiency. Markdown can save time and tokens when AI agents parse documentation. It gives agents clean structure instead of forcing them through complex HTML, navigation, ads, and JavaScript.

Vercel says 10% of its signups come from ChatGPT. Its llms.txt includes contextual API descriptions that help agents decide what to fetch.

That matters, but mostly for developer tools and API documentation. If your audience uses AI coding assistants like Cursor or GitHub Copilot to interact with your product, token efficiency can improve integration.

For ecommerce brands selling pet supplies, insurance companies explaining coverage, or B2B SaaS companies targeting nontechnical buyers, token efficiency does not automatically translate into traffic.

llms.txt is a sitemap, not a strategy

The closest comparison I can make is a sitemap.

Sitemaps are useful infrastructure. They help search engines discover and index content more efficiently. But I would not credit traffic growth to simply adding a sitemap. The sitemap documents what exists; the content drives discovery.

Llms.txt works in a similar way. It may help AI models parse a site more efficiently if they choose to use it, but it does not make the content more useful, authoritative, or likely to answer user queries.

In my analysis, the sites that grew did so because they:
- Created functional assets such as downloadable templates, comparison tables, and structured data.
- Earned external visibility through press and backlinks.
- Fixed technical barriers such as crawl and indexing issues.
- Published content optimized for extraction, including FAQs and structured comparisons.
Llms.txt documented those efforts. It did not drive them.

What actually works

The two successful sites showed me what actually matters.
- Create functional, extractable assets. The SaaS platform built 27 downloadable templates that users could deploy immediately. AI models surfaced them because they solved real problems, not because they appeared in a markdown file.
- Structure content for extraction. The neobank rebuilt product pages with comparison tables for interest rates, fees, and account minimums. That is data AI models can pull directly into answers without heavy interpretation.
- Fix technical barriers first. The neobank fixed crawl errors that had blocked content for months. If AI models cannot access your content, no amount of documentation will help.
- Earn external validation. Coverage from Bloomberg and other major publications drove referral traffic, branded searches, and likely influenced how AI models assessed authority.
- Optimize for user intent. Both sites answered specific queries, such as “best project management templates” and “how do [brand] interest rates compare?” Models surface content that maps to what users ask, not content that is merely well documented.
None of this requires llms.txt. All of it can drive results.

Should you implement an llms.txt file?

If you run a developer tool and AI coding assistants are a primary distribution channel, I would implement llms.txt. In that context, token efficiency matters because your audience is already using agents to work with documentation.

For everyone else, I would treat llms.txt like a sitemap: useful infrastructure, not a growth lever.

It is good practice to have. It likely will not hurt. But the hour spent implementing llms.txt is often better spent restructuring product pages with extractable data, publishing functional assets, fixing technical SEO issues, creating FAQ content, or earning press coverage.

Those tactics have shown real ROI in AI discovery. Llms.txt has not, at least not yet.

The lesson I take from this is not that llms.txt is bad. It is that we are reaching for control in a system where the rules are still being written. Llms.txt offers comfort because it is concrete, actionable, and familiar. It looks like the web standards we already understand.

But looking like infrastructure is not the same as functioning like infrastructure.

My focus would stay on what is already working:
- Create useful content.
- Structure it for extraction.
- Make it technically accessible.
- Earn external validation.
Platforms and formats will change. The fundamentals will not.

Inspired by this post on Search Engine Land.
July 3, 2026
Unlocking AI Visibility: Top Agencies for LLM Success

As someone deeply engaged in the world of AI, I’m excited to share how leading agencies are empowering brands to achieve AI visibility, optimize LLM citations, and maintain discoverability across tools like ChatGPT, Gemini, and Perplexity.

These expert agencies are paving the way for businesses to thrive in an AI-driven landscape, ensuring brands don’t just survive but excel in AI search environments.

Inspired by this post on HiGoodie Blog.

April 1, 2026
How AI Search Engines Prefer Reddit, YouTube, and LinkedIn
During a recent study, I discovered that Reddit stands out as the most-cited domain in AI-generated answers. In fact, it’s ahead of heavyweights like YouTube and LinkedIn, thanks to an analysis of 30 million sources conducted by Peec AI, a tool specializing in AI search analytics.

The findings: I’ve learned that Reddit claims the top spot across various AI platforms including ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. Top contenders YouTube, LinkedIn, Wikipedia, and Forbes are right behind. Platforms like Yelp and G2 frequently appear when searching for recommendations.

As I delved deeper into the research, it became clear which domains the AI models tend to lean on:
- ChatGPT values Wikipedia, Reddit, and editorial sites like Forbes.
- Google shows preference for platforms such as Facebook and Yelp.
- Perplexity favors Reddit, LinkedIn, and G2 for queries within the B2B realm.
Why we care: The insight that resonated with me was the importance of having authority beyond just our own websites. Brands that consistently feature on reputable third-party platforms have a better chance of being cited by AI.

Why these sources? It’s fascinating to see how AI systems are wired to prioritize both authority and authentic user input:
- I’ve found that Reddit excels because it mirrors genuine user discussions.
- YouTube shines in video citations, owing to their comprehensive transcripts and descriptions.
- Wikipedia not only serves real-time data but also acts as a foundation for training datasets.
About the data: The analysis spanned 30 million sources, providing a comprehensive look at how often domains are directly cited in AI answers, effectively revealing what shapes these responses.

The study. For those interested in a deep dive, the full study is available here: Top domains cited by AI search: Analysis based on 30M sources

Dig deeper. For more on citation research, check out these fascinating reads:
- AI citations favor listicles, articles, product pages (March 2026)
- Only 15% of pages retrieved by ChatGPT appear in final answers (March 2026)
- ChatGPT citations favor a small group of domains (March 2026)
- Google’s AI Mode is citing Google more than any other site (March 2026)
- 44% of ChatGPT citations come from the first third of content (February 2026)
- AI Overview fan-out rankings boost citation odds by 161% (December 2025)
- Tracking AI search citations: Who’s winning across 11 industries (October 2025)
- YouTube dominates AI search with 200x citation advantage (October 2025)
- AI search relies on brand-controlled sources, not Reddit (October 2025)
- How to get cited by AI: SEO insights from 8,000 AI citations (May 2025)
- 82% of Google AI Overviews citations come from deep pages (March 2025)
Inspired by this post on Search Engine Land.
March 31, 2026
Uncovering AI’s Citation Preferences: Listicles Lead the Way

I recently delved into a fascinating study exploring how AI citations are significantly influenced by certain content formats. It turns out listicles, articles, and product pages are at the forefront, driving over 52% of mentions across various AI language models.
The research, conducted by Wix Studio AI Search Lab, analyzed a whopping 75,000 AI answers and more than a million citations across platforms like ChatGPT, Google AI Mode, and Perplexity. It’s an exciting revelation that showcases the power of content structure in digital landscapes.
The findings? Listicles claimed the top spot with 21.9% of citations, followed by articles at 16.7% and product pages at 13.7%. When combined, these formats make up a majority of the citations AI references.
What’s interesting is that articles tend to dominate when it comes to informational queries, being cited 2.7 times more than other formats. Meanwhile, listicles capture nearly 40% of commercial-intent citations, almost double compared to any other type.
The Why Behind Intent. It’s fascinating to see how query intent, rather than industry or AI model, is the strongest predictor of which content gets cited. This trend doesn’t shift much across different sectors, from SaaS to health industries.
Informational queries skew towards articles (45.5%) and listicles (21.7%), while commercial queries are dominated by listicles (40.9%). Interestingly, transactional and navigational queries favor product and category pages, with those two formats comprising about 40% of the citations combined.
The Impact for Us. This study is incredibly insightful, illustrating why aligning content types with user intent is more strategic than simply generating content. Articles serve to inform, listicles foster comparisons, and product pages drive conversions. Tailoring content to align with user goals might just be the key to snagging more AI citations and enhancing visibility.
Not all listicles perform equally. In professional services, third-party listicles account for 80.9% of citations, showing a preference for neutral editorial comparisons over branded lists by large language models.
Looking at Model Preferences. While all models have a penchant for listicles, their other preferences vary. ChatGPT leans heavily towards articles and informational content, Google AI Mode shows a balanced approach, and Perplexity stands out with 17% of its citations coming from discussions on platforms like Reddit and forums.
Industry-Specific Trends. Though preferences shifted slightly among industries, there are notable trends. SaaS and professional services veer towards listicles, health sectors favor authoritative articles, and ecommerce spreads its citations across listicles, articles, and category pages. Interestingly, home repair maintains an even distribution across different formats.
I’m intrigued to know more! The comprehensive research can be found here.

Inspired by this post on Search Engine Land.

March 24, 2026
Discover iOS Comet Browser: Blending Google Search & AI Excellence

I’ve recently discovered Perplexity’s innovative Comet browser for iOS, which defaults to Google Search. It makes perfect sense, given that mobile users typically focus on navigating, finding local results, and completing transactions. As Perplexity CEO Aravind Srinivas points out, “Google does a much better job … than anyone else … including Perplexity.”

Comet for iOS. This browser integrates Perplexity’s AI assistant directly, providing a seamless experience. It cleverly merges AI-generated answers with standard search outcomes, so for numerous queries, you won’t miss the familiar results page.

While browsing, I can query using my voice, which is incredibly convenient. The assistant’s capabilities include summarizing entire pages, answering questions, and even drafting emails on my behalf.

One feature I find particularly useful is Deep Research, which generates cited summaries and prepares materials tailored for serious inquiry.

What Comet does. The assistant can take action on my behalf. Among other things, it excels at summarizing articles and sharing outputs, researching people or topics across tabs, and assisting with bookings or filling out forms. It’s like having a digital personal assistant ready at all times.

What Perplexity is saying.

“The search experience in Comet iOS provides traditional search result pages for fast, local, and high-intent queries that are more common on mobile. Meanwhile, the Comet Assistant easily allows for more advanced knowledge and intelligence powered by the Perplexity answer engine. The intention is for users to have the smoothest browsing experience possible for the real use cases of iOS.”

Why we care. As search continues to evolve towards hybrid models, optimizing for both traditional Google results and AI-generated responses becomes crucial. This shift underscores Google’s stronghold in commercial and local search, while driving the competition into the AI domain.

The announcement. Comet is Now available on iOS

Inspired by this post on Search Engine Land.

March 19, 2026

Unveiling GEO: How AI Chatbots Shape Product Recommendations

Last updated: March 12, 2026

As I dive into the intriguing world of Generative Engine Optimization (GEO), I find myself exploring how we can fine-tune a company’s online presence to have their products or services recommended by generative AI chatbots. Although still a budding marketing avenue, GEO’s potential reminds me of the early days of SEO, ripe for exploration and growth. I’m convinced that the deep insights from this research will pave the way for much-needed best practices in the market.

My team and I embarked on an extensive study from March 2024 through December 2025, focusing on the recommendation algorithms of the four most popular generative AI chatbots in the United States. We meticulously conducted 11,128 commercial queries across various sectors, seeking to unravel the factors these chatbots use to recommend products and services. We’ve continued to update our insights, the latest being on March 12, 2026.

The table below gives a detailed breakdown of our research findings, listing the factors influencing chatbot recommendations in terms of weight. Following the table, I delve into each factor, elucidating how each chatbot incorporates them into their recommendation process.

```json
{
"alt": "Forum discussion listing about the best CRM tools with user reviews from Reddit and Quora.",
"caption": "Explore popular forum discussions comparing the best CRM tools, with user insights from Reddit and Quora.",
"description": "This image displays a set of forum posts from Reddit and Quora discussing the best CRM tools available. The Reddit post features user comments about Hubspot and ZOHO, highlighting pros and cons. Quora threads inquire about recommended customer relationship management tools. The image helps users evaluate CRM tools based on community feedback and comparisons. Keywords: CRM, forum, Reddit, Quora, Hubspot, ZOHO."
}
```

Generative AI Engine	U.S. Market Share*	Algorithm
ChatGPT	61.3%	Authoritative list mentions: 41% Awards, accreditations, & affiliations: 18% Online reviews: 16% Customer examples & usage data: 14% Social sentiment: 11%
Google Gemini	13.3%	General Searches Authoritative list mentions: 49% Google website authority: 23% Awards, accreditations, & affiliations: 15% Online reviews: 13% Local Searches Local business reviews: 38% Authoritative list mentions: 29% Online reviews: 19% GBP website authority: 14%
Perplexity	3.1%	General Searches Authoritative list mentions: 64% Online reviews: 31% Award, accreditations, & affiliations: 5% Local Searches Local business reviews: 39% Authoritative list mentions: 34% Online reviews: 27%
Claude AI	2.5%	Traditional databases & directories: 68% Awards, accreditations, & affiliations: 19% Customer examples & usage data: 13%

*Source: Generative AI Chatbots by Market Share

Generative Engine Ranking Factors

Allow me to take you through the key factors that guide commercial recommendations across these generative engines. Although they share common factors, each employs a unique weighting system to determine recommendations.

```json
{
"alt": "Google review of First Page Sage with a 5-star rating and customer comment.",
"caption": "Raving reviews for First Page Sage! Their SEO expertise is applauded with a perfect 5-star rating and customer satisfaction.",
"description": "The image shows a Google review page for First Page Sage, located at 2250 Union St, San Francisco, CA, featuring an impressive 5.0-star rating from 8 reviews. Highlighted user comments mention key services like SEO, lead generation, strategy, and content creation. A featured review praises their expertise in elevating marketing efforts, emphasizing the team's knowledge and up-to-date practices."
}
```

NOTE: The more advanced versions of these AI chatbots may personalize their suggestions as more personal data is provided, potentially altering factor weightings.

Authoritative List Mentions

When it comes to predicting content, generative AI engines draw information from multiple authoritative sources. They echo the voices of experts, offering recommendations rooted in well-regarded lists and rankings. I find it fascinating how they lean heavily on top-ranking Google searches to refine their recommendations, which are potently informed by these highly authoritative sources.

Claude stands apart, favoring traditional compendiums over Google-reliant lists, perhaps embracing a more traditional approach.

Awards, accreditations, and affiliations

Mentioning an award or accreditation on a trustworthy website signals authority, nudging AI to recommend the associated company or product more frequently. It’s quite interesting to see this recognition elevated in the virtual world.

```json
{
"alt": "Text detailing top generative engine optimization agencies for 2025, highlighting First Page Sage.",
"caption": "Discover the leading GEO agencies of 2025, like First Page Sage, leveraging AI to revolutionize SEO and maximize visibility in generative engines.",
"description": "An image listing top generative engine optimization (GEO) agencies as of mid-2025, highlighting the integration of traditional SEO with AI tools like ChatGPT and Gemini. It emphasizes agencies like First Page Sage, noted for their strong content strategy and leadership in 'thought leadership SEO.' Based in San Francisco, CA, First Page Sage is recognized for its high-editorial standards and AI integration, showcasing their commitment to excellence in the evolving landscape of generative engines."
}
```

Online Reviews

Online reviews hold substantial sway for ChatGPT, Gemini, and Perplexity, especially reviews from platforms like Amazon, Better Business Bureau, and Glassdoor. I see how a confluence of positive reviews can significantly boost recommendation weight.

Social Sentiment

```json
{
"alt": "Text outlining top affordable lawnmowers under $1,000, highlighting Honda, Toro, and Craftsman models.",
"caption": "Discover top lawnmowers under $1,000! Uncover powerful features and savings with Honda, Toro, and Craftsman models for a perfect cut.",
"description": "This text image lists three highly-rated lawnmowers under $1,000: the Honda HRX217VKA, Toro Recycler 20333, and Craftsman M310. It describes each model’s engine performance, cutting deck size, and unique systems like Honda's Versamow and Toro's Personal Pace. Ideal for those seeking balance in performance and affordability. Keywords: lawnmowers, Honda, Toro, Craftsman, gardening tools, budget-friendly."
}
```

It’s intriguing to see how the way a company is discussed online, including on news sites and social platforms like Reddit, subtly shapes ChatGPT’s recommendations. Its current influence is modest but poised for growth as trust builds in digital communities.

Customer Examples & Usage Data

Recognized endorsements and partnerships visibly boost a product’s credibility. This factor, used by ChatGPT and Claude, reinforces the reputational weight of significant customer associations or user data.

```json
{
"alt": "Two doughnut charts comparing Gemini's general and local recommendation algorithms with different factors like online reviews and website authority.",
"caption": "Exploring Gemini's Algorithms: A visual breakdown of Gemini's general and local recommendation algorithms, highlighting the role of reviews, website authority, and list mentions.",
"description": "This image features two doughnut charts illustrating the differences between Gemini's General and Local Recommendation Algorithms. The General Algorithm chart includes factors like Authoritative List Mentions (49%), Google Website Authority (23%), Awards (15%), and Online Reviews (13%). The Local Algorithm chart shows Local Business Reviews (38%), Authoritative List Mentions (29%), Online Reviews (19%), and GBP Website Authority (14%). Keywords: recommendation algorithms, online reviews, website authority, local business."
}
```

Google Website Authority

Google attributes site authority based on factors like consistent content publication. Gemini values this significantly, drawing from Google’s well-established credibility measures.

Local Business Reviews

```json
{
"alt": "List of top custom software development firms ranked by expertise and industry specialization.",
"caption": "Discover leading custom software development firms excelling in innovation, technology, and industry-specific solutions, recognized by DesignRush and Sphinx Solutions.",
"description": "This image presents a list of top custom software development firms segmented into general top-ranked and industry-specific experts. Instinctools and Andersen are noted for their broad and comprehensive capabilities across technologies and FinTech, respectively. Instinctools has over 1000 successful projects, while Andersen boasts global reach with over 11 offices. Firms with industry expertise, such as Fingent Global Solutions and Onesoft Technologies, are highlighted for their specialized services in sectors like accounting, HR, and e-commerce, with significant presence in markets like India. Mentioned recognitions include DesignRush and Sphinx Solutions."
}
```

For local queries, Gemini and Perplexity lean on reviews from Google Business Profiles and Yelp. This localized trust mechanism brings a nuanced understanding to the recommendation landscape.

Traditional Databases & Directories

Generative AI chatbots like Claude often delve into established resources like encyclopedias and business directories. This approach weights well-established data heavily in crafting precise business recommendations.

```json
{
"alt": "CeraVe Moisturizing Cream container shown, highlighting popular moisturizer choice with ceramides and hyaluronic acid for dry skin.",
"caption": "Discover the skin-loving benefits of CeraVe Moisturizing Cream, a dermatologist-recommended favorite for dry skin, featuring ceramides and hyaluronic acid.",
"description": "The image features a container of CeraVe Moisturizing Cream, a leading moisturizer known for its fragrance-free and dermatologist-recommended formula. It contains essential ceramides and hyaluronic acid to strengthen the skin barrier and retain moisture, making it suitable for all skin types, including those with eczema. Its affordability and effectiveness contribute to its popularity among consumers looking for quality skincare solutions. Keywords: CeraVe, moisturizer, ceramides, hyaluronic acid, skincare."
}
```

ChatGPT’s Recommendation Algorithm

In my exploration of ChatGPT’s algorithm, I’ve noticed its reliance on Bing to gather authoritative lists, reviews, and rankings. It aggregates and refines recommendations through a blend of sources, ensuring a comprehensive outcome.

Often, top Bing search results heavily guide its recommendations, but in their absence, ChatGPT factors in alternative data like awards, reviews, and social sentiment. An illuminating example involved its interpretation of lawnmower choices guided largely by trusted reviews from notable publications.

```json
{
"alt": "Two pie charts compare Perplexity's General and Local Recommendation Algorithms showing shares of authoritative mentions, online reviews, and more.",
"caption": "Compare the influences on Perplexity’s General vs. Local Recommendation Algorithms. Explore the impact of online reviews, authoritative list mentions, and more.",
"description": "This image presents a comparison between Perplexity's General and Local Recommendation Algorithms through two pie charts. The left chart highlights the General Algorithm with 64% authoritative list mentions, 31% online reviews, and 5% awards and affiliations. The right chart for the Local Algorithm showcases 34% authoritative mentions, 27% online reviews, and 39% local business reviews. Each segment is color-coded for easier distinction, offering a visual insight into the factors affecting recommendation algorithms."
}
```

Google Gemini’s Recommendation Algorithm

Gemini’s algorithm intrigues me with its Google-centric approach, harnessing authority and reviews together from search results to guide recommendations. Its unique method prioritizes recognized achievements, often steering clear of poorly reviewed companies.

In practical application, Gemini reinterprets product searches by balancing authority with popularity, evidenced by its moisturizer recommendations, aligning sales volume with positive reviews.

```json
{
"alt": "Donut chart showing distribution of ClaudeAI's recommendation algorithm with three segments: Traditional Databases, Customer Examples, and Awards.",
"caption": "Explore ClaudeAI's Recommendation Algorithm: A visual breakdown showing the dominance of traditional databases, complemented by customer examples and accredited awards.",
"description": "This donut chart illustrates the components of ClaudeAI's recommendation algorithm. The largest segment, at 68%, is Traditional Databases & Directories, shown in purple. Customer Examples & Usage Data represent 13% in green, while Awards, Accreditations, and Affiliations make up 19% in red. The chart provides insights into the diverse data sources informing ClaudeAI's recommendations."
}
```

Perplexity’s Recommendation Algorithm

What strikes me about Perplexity is its straightforward algorithm, largely favoring search lists and reviews. It often taps into the most readily available online viewpoints to construct its recommendations.

For local business queries, its focus on high-ranking lists underscores a strategy based on easily established credibility from popular review sites.

```json
{
"alt": "Screenshot of a text providing names of well-known travel agencies in the US.",
"caption": "Discover the top US travel agencies as recommended by an AI, featuring American Express Travel, AAA Travel, and Liberty Travel.",
"description": "This image is a screenshot of text listing well-known travel agencies in the United States, including American Express Travel, AAA Travel, and Liberty Travel. The text clarifies that these names are based on the AI model's knowledge as of August 2023 and not current rankings. Useful for travelers seeking trusted agencies, this content is informative about travel planning options in the US."
}
```

Claude AI’s Recommendation Algorithm

Unique in its approach, Claude AI depends on traditional databases, often highlighting historically established companies in its recommendations. This somewhat conservative method gives it a distinct identity in the generative AI landscape.

Focused purely on national businesses, it bypasses local recommendations altogether, streamlining its efforts towards broader-scale authority.

Downloading This Report & Inquiries

If you’re curious to learn more or desire a PDF copy of this report, please reach out via our contact page.

First Page Sage is also at the forefront of GEO services. Interested in knowing more? Don’t hesitate to contact us.

Inspired by this post on First Page Sage Blog.

March 12, 2026

Harnessing SEO for ChatGPT’s Unprecedented Growth
I embarked on an SEO audit exploring how platforms like ChatGPT, Claude, and Perplexity leverage technical optimization, content, and conversions to scale their operations.

Generative search engines, such as ChatGPT, have cleverly woven SEO into their growth strategies. Despite claims to the contrary, these platforms have not abandoned this vital marketing channel.

I was curious to learn how well ChatGPT, Perplexity, and Claude are doing in the SEO realm, and what makes ChatGPT’s dedication to this strategy so effective.

ChatGPT’s annual investment in SEO, estimated at $600,000, is yielding significant returns for generative AI platforms. With Semrush data showing ChatGPT’s monthly organic traffic at 76.5 million visits, and with a conservative conversion rate of 0.5% at a $20/month entry price, I foresee a potential annual revenue of around $92 million (a remarkable 15,200% ROI) for ChatGPT.

Both Claude and Perplexity also showcase positive returns, albeit more modestly, ranging from 82% to 240% ROI, highlighting the persuasive potential of SEO investment.

OpenAI has shown great foresight by investing heavily in SEO and content, offering up to $393,000 annually for an SEO-savvy content strategist. This significant investment underscores how seriously OpenAI takes the role of SEO in its growth strategy.

Additionally, they’ve pursued roles centered on growth, SEO, CRO, and web strategy, offering salaries between $410,000 and $600,000 for two essential roles, excluding benefits and other costs. Their commitment to SEO showcases the profound belief in its capacity to act as a cornerstone for expansion.

SEO, a tool as versatile as it is durable, taps into human behavior — a fundamental necessity for survival instincts like searching for food or shelter. By extension, search engines elevate this natural behavior.

ChatGPT is expanding search behavior, amplifying the amount of Google searches within select contexts. Despite a 20% decrease in Google’s search volume from 2024 to 2025, it’s clear visibility is increasingly crucial.

The OpenAI team is acutely aware of this evolution and has decisively incorporated SEO into the architecture of ChatGPT.

Inspired by the insights from a competitive keyword analysis via Semrush, I delved into the authority, keyword distribution, and rankings across ChatGPT, Perplexity, and Claude. ChatGPT leads with a formidable authority score of 99, far ahead of Perplexity (81) and Claude (75), setting a benchmark for deriving authority through robust public relations and strategic media visibility.

The journey through the keywords and paid versus organic strategies highlights an under-recognized opportunity: integrating search strategies could optimize conversions and reduce PPC acquisition costs, significantly boosting brand presence.

Gleaning Key Insights:
- ChatGPT indexes approximately 287,800 keywords.
- Perplexity follows with around 184,800 keywords.
- Claude trails with about 36,000 keywords.
ChatGPT capitalizes on user-generated content, while Perplexity and Claude focus on niche, high-intent professional content. However, ChatGPT stands distinguished due to its alignment of strong branding and robust SEO.

Using our agency’s 3Cs SEO and AI optimization framework — code, content strategy, and conversions — I emphasize the importance of optimizing key technical components like the robots.txt file and URL structures that significantly influence search rankings.

In examining content, there’s a considerable gap in SEO optimization on pages from Perplexity and Claude, evident in their oversight of meta titles, descriptions, URLs, and tag optimizations, leading to some not even being indexed by Google.

Leveraging descriptive image names and integrating user-generated content could further bolster search engine performance, as demonstrated by ChatGPT’s steady keyword ranking growth.

Understanding conversions’ role, I see that these platforms seamlessly convert trial users into paying customers by offering trial access before prompting a commitment.

The Road Forward: Optimization remains a never-ending journey. By aligning with OpenAI’s successful model, businesses can bet on SEO as a dynamic component of growth strategies. As the landscape evolves, so should our tactics to ensure visibility and conversion remain at the forefront.

Inspired by this post on Search Engine Land.
February 25, 2026
Discover Unique Citation Patterns of AI Platforms

I recently came across fascinating research revealing how diverse AI platforms like ChatGPT, Google AI, and Perplexity cite their sources. It’s intriguing to see how each platform approaches sourcing information and the implications for their visibility.

The study highlights substantial differences in citation patterns among these major AI players. This variation in sourcing methods significantly affects how each platform is perceived in terms of reliability and authority.

Understanding these citation patterns can offer valuable insights into the competitive landscape of AI visibility. As we explore this further, it becomes clear why recognizing these differences is crucial for anyone interested in AI optimization.

Inspired by this post on Try Profound Blog.

February 24, 2026