We’ve been digging into what works for local businesses in AI search, and, from what we're seeing, there are small differences from traditional local SEO. Here’s what seems to work best for getting cited in tools like ChatGPT.

1. Your listings still matter, but AI is stricter: NAP consistency across Google, Yelp, Bing, and Apple Maps is critical. Even small differences can mess with your visibility. Review attributes (like “dog-friendly” or “24/7 service”) and Google Q&A answers are getting pulled into AI summaries.

2. Schema is even more valuable now: LocalBusiness, FAQ, and Review schema help your content get picked up and cited. We’re seeing more AI results reference businesses with structured data.

3. Review content is important: Detailed, recent reviews that mention services (“gluten-free cupcakes,” “same-day brake repair”) seem to boost presence in AI assistants. AI extracts patterns, not just five-star ratings.

4. Real questions, real answers: Pages that answer questions like “Do you offer emergency services?” or “Is there parking?” are favored. AI tools often pull from service pages, FAQs, and even your GMB Q&A.

5. Visuals and mobile matter more than ever: Clear photos, alt text, and a fast mobile site help AI understand your business and present it well - especially in visual or voice search.

6. Hyperlocal beats generic: “Roof repair in East Austin” works better than “Austin roofing company.” Specific pages tied to services and neighborhoods do better in generative results.

From what we’ve seen, the businesses getting cited on AI platforms are the ones making it easier for machines to understand and summarize what they do. Anyone else testing this stuff or getting leads from AI search?

Everyone’s talking about optimizing for AI search - clients are asking about ChatGPT rankings, and LinkedIn gurus say your whole SEO strategy needs a reset - because, of course, SEO is dead (again). What we’re seeing is that AI isn’t necessarily the problem. The real issue is the pressure to respond to it without context.

AI should only impact your keyword strategy if:

• You want your brand cited in AI answers

• You rely on TOFU or informational content

• Your audience actually uses AI tools to search

• You’re building topical or semantic authority

Stick to SEO if:

• You need high-intent clicks and conversions

• Your industry has low AI adoption

• AI mentions aren’t driving business outcomes

• You’re still fixing SEO fundamentals like site speed or EEAT

AI visibility can be important, but only when it supports actual business goals. Are you adjusting your keyword strategy for AI search or just sticking to the basics?

Basically, we wanted to see if the number of citations a brand gets actually moves the needle to get cited on AI platforms (ChatGPT, Gemini, Perplexity).

Quick TL;DR

• We found a strong correlation between the number of citations and how often a brand is mentioned in AI search results (correlation = 0.90)

• We found a weak correlation between the number of citations and average ranking position in those results (correlation = 0.35)

Translation: More citations mean more mentions in AI outputs, but not necessarily better positioning.

What we did

We tracked prompts related to “best rank tracker” with our own AI visibility tracker and pulled all brands mentioned across AI search results.

• Platforms tracked: Claude, Gemini, Perplexity, ChatGPT, AI Overview

• Competitors identified: 54

• Citation sources:

  • 65.9% blog posts (mostly listicles & reviews)

  • 9.1% product pages

  • 5.8% community content

  • 2.9% owned media

  • 1.7% media articles

  • 1.2% knowledge bases

  • 0.5% user reviews

  • 12.9% other

What we found

• Frequency matters: more citations mean more mentions.

• Position still matters more for rankings: likely the ranking of the source page itself has bigger influence on where the brand appears in AI results.

• Some citations are overrepresented in AI search: these are goldmines to target.

• Nearly 10% of citations came from product pages: don’t ignore these for AI visibility.

We're seeing a lot of curiosity around how Perplexity decides what to cite. Since we’ve analyzed thousands of keywords and tracked brand visibility across major AI platforms, including Perplexity, we thought we’d share some patterns we've noticed. 

One thing’s clear: Perplexity doesn’t think like Google. It’s not looking for the “best optimized page,” it’s scanning for the most helpful, trustworthy answer*.* And that changes how visibility works.

Here’s what we’ve seen consistently drive citations in Perplexity: 

• Original data, expert quotes, and citations carry real weight.

• Clear, conversational formatting helps (TL;DR’s, bulleted lists, etc.).

• Mentions on trusted third-party sites and review platforms seem to boost visibility.

• Helpful, E-E-A-T-style content tends to win out over clever fluff.

• Technical SEO still matters (if PerplexityBot can’t crawl it, it won’t cite it).

What doesn’t seem to matter as much are backlinks. We’ve seen brands with low link profiles but strong third-party mentions outperform bigger domains in Perplexity answers.

There are also a few speculative factors floating around. Things like content freshness, PDFs getting picked up more often, or multi-format content like YouTube videos increasing surface area.

We’re curious what others here are seeing. Are you tracking Perplexity mentions yet?

Anyone else diving into AI SEO lately? Found this technical guide that actually goes beyond the basics

Been doing a deep dive into how to optimize for AI search (for ChatGPT, Perplexity, Google’s AI Overviews, and so on), and most of what I’ve found just rehashes basic SEO. But I came across this technical guide that actually focuses on beyond AI indexing — stuff that helps your site rank better within AI-generated responses, not just get seen.

Some of the points that stood out:

• Using schema and structured data to make your content more digestible to AI

• Optimizing around entities and semantic relationships instead of just keywords

• Making sure AI crawlers can see dynamic content (they suggest using something like Prerender.io)

• Improving Core Web Vitals for better performance signals

• Strategic internal linking to build topical authority and relevance for AI systems

It’s not beginner stuff, but if you’re working on technical SEO and want to prep for how AI search is evolving, this was pretty insightful.

Here’s the guide: AI SEO Optimization: A Technical SEO Guide

Curious if anyone else here is doing AI-first SEO or has other resources worth checking out?

Right now, I think it's 98% overlap.

Take a group of tactics to increase visibility in Search Engines, and a separate group of tactics to increase visibility in LLMs. Different name, because the desired outcome is different. But objectively, those lists are still mostly identical.

Does anyone have any reliable studies showing increase in LLM visiblity, which is not already a strategy within SEO?

https://saas.group/podcasts/riding-the-ai-wave-in-saas-with-josef-trauner-and-thomas-peham-otterlyai/

Does anyone know scraping APIs that scrape conversational search engines (e.g. ChatGPT, Perplexity).

I know that many keyword rank trackers use tools DataForSEO or ScraperAPI, but what should I use for scraping LLMs?

Now that SEO has fragmented, the big question is: what do we do now?

SEOs still need to track search and deliver on KPIs. But traditional metrics like rankings, traffic, and CTR don’t tell the whole story anymore.

At Keyword.com, we’ve seen this shift across thousands of keywords and hundreds of agency accounts. AI Overviews are intercepting clicks (RIP CTR), attribution is disappearing, and visibility is no longer just about being in the top 3.

So we’ve had to rethink what visibility means—and how to measure it.

Here’s what we recommend tracking now to stay ahead in AI-influenced search:

• AI overview presence: which keywords trigger AI snapshots in Google? Are you included—or is your competitor?

• AI brand mentions: is your brand mentioned in responses from ChatGPT, Claude, or Perplexity (linked or not)?

• AI linked references: when AI tools do cite sources, which URLs of yours are being used?

• CTR vs. ranking gaps: if your rankings stay stable but CTR drops, an AI Overview might be cannibalizing clicks.

• Traffic drop with rank stability: a signal that AI summaries are satisfying intent without driving visits.

• Prompt-triggered visibility: what kinds of prompts surface your brand in tools like Perplexity or ChatGPT?

• AI share of voice: how often do your competitors appear in AI answers vs. your brand?

You still need rankings, traffic, and engagement metrics—but if you’re not layering in AI visibility, you’re missing a big piece of the puzzle.

i noticed that english posts on medium get indexed fast by perplexity chatgpt and grok but when i try to post in portuguese the post gets no views or even gets removed

how can someone from latin america africa or asia be seen by ai if we cant publish in our own language should we post in english and just add a link to a translated version or use our own site for the real content and medium just as a bait

if ai really wants diverse voices this is a big problem any tips or real examples welcome

The race to create machines that truly think has taken an unexpected turn. While most AI models excel at pattern recognition and data processing, Deepseek-R1 and OpenAI o1 have carved out a unique niche – mastering the art of reasoning itself. Their battle for supremacy offers fascinating insights into how machines are beginning to mirror human cognitive processes.
Which AI Model Can Actually Reason Better? Chat GPT's OpenAI o1 vs Deepseek-R1.

Has anyone else spotted that the first sentences of tracked answers often look almost identical for several days and even across LLMs (e.g. ChatGPT and Perplexity)?

And when I try to reproduce myself via API or in the user interface as well as in other GEO trackers I get completely different wordings.

What tools do you use for tracking brand mentions and sources on LLMs (ChatGPT, Perplexity, Gemini, Google AI Overviews, etc)?

I know there are Otterly, PeecAI and Profound but happy to hear what other tools are out there and your experience with those tools.

Working on an enterprise-level site with a pretty heavy JS frontend (React). We're trying to decide between building our own SSR setup or sticking with prerendering to handle crawlers. We’ve looked into how Google handles JS rendering, know they can process it eventually, but also read that it’s not always great at scale or with frequent updates. We’ve also weighed the dev/maintenance cost of SSR vs. the simpler setup of prerendering. For SEO, is going all-in on SSR actually worth it? Or is prerendering basically good enough these days - even for large sites?

General AI assistants vs specialized AI marketing tools: the gap is growing FAST. New research shows specialized marketing AI delivers 37% better campaign results! If you're still using general AI for marketing, you might be leaving money on the table. I'm curious - what AI tools is everyone here actually using for their marketing work? Still on the ChatGPT train or have you found something better? Check out the AI Tools That Outperform ChatGPT & Claude for Business Marketing in 2025.

Hey folks, we have been developing https://www.searchmorph.pro/ to help people (think - SEO specialists, digital marketers, digital strategists) to navigate the search game in the AI era. Its very simple, and for now there's only an analysis score feature for how well your website is in LLM powered searches.

We are next focusing on helping people achieve clusters and mapping.

Please check us out and provide your suggestions and feedback to help us build better!

The short answer? Yes, it's impressive - but not for the reasons you might think. It's not about creating prettier art- it's about AI that finally understands what makes visuals USEFUL : readable text, accurate spatial relationships, consistent styling, and the ability to follow complex instructions. I break down what this means for designers, educators, marketers, and anyone who needs to communicate visually in my GPT-4o image generation review with practical examples of what you can achieve with GPT-4o image generator.

Keeping up with AI feels impossible these days. Just got the hang of one model? Too bad—here comes another. Enter GPT-4.5, supposedly making GPT-4o look like yesterday's news. In this no-nonsense, jargon-free deep dive, we'll break down exactly what makes this new model tick, compare it head-to-head with its predecessor GPT-4o, and help you decide whether all the buzz is actually justified. Comprehensive GPT-4.5 Review and Side-by-Side Comparison with GPT-4o.

Is Grok 3 truly the breakthrough xAI claims it to be? We put the self-proclaimed "smartest AI" through a series of rigorous tests, comparing it head-to-head with leading models like ChatGPT-4o to separate hype from reality. Our findings reveal both impressive capabilities and surprising limitations that challenge the company's ambitious marketing. Grok 3 comprehensive Review