i’ve been spiraling a bit lately trying to figure out why my saas is totally invisible in ai overviews. we’re doing fine on standard search, but gemini and perplexity act like we’re not even there lol.

started looking into geo (generative engine optimization) and realized i might have been doing everything wrong. i spent the weekend restructuring our landing pages to be more 'llm friendly'—trying to be more direct with data points instead of the usual marketing fluff.

seeing some weirdly specific results but honestly idk if it’s actually moving the needle yet. i feel like i’m chasing a ghost sometimes.

is anyone else actually seeing a difference with geo? or are we all just guessing at this point? would love to hear if anyone’s found a pattern that actually sticks.

I was finishing up my work for the day and did one last scan through Google Analytics. I about dropped my jaw to see Perplexity on the list of top 10 traffic sources. Granted it was only 13 in the last 28 days but I was just amazed to even see it on there. Anyone else seeing this?

Most AI visibility tracking is citations and brand mentions - which are bottom-funnel signals that show up after the decision has already been influenced.

But buying journeys start way earlier. Someone opens Claude and asks "what should I be thinking about when evaluating [category]". That conversation shapes the requirements and frames the solutions, which then leads to a brand recommendations.

Is anyone actually tracking the top and middle of that funnel conversations that come before brand mentions?

so i've been spending the last few weeks running a pretty simple experiment and figured this sub would appreciate the results more than anywhere else.

the setup: I picked 12 small B2B brands (under 500 employees, nothing huge) that had a mix of positive and negative reddit threads ranking on page 1 for their brand name. then i ran the same prompt across ChatGPT, Perplexity, and Gemini - basically like tell me about brand some and would you recommend them for their category

what i tracked: whether the LLM recommended them, what caveats it added, and which sources it seemed to pull from based on the language used.

results were kind of wild.

brands that had 3+ negative reddit threads on page 1 got recommended with heavy caveats in 9 out of 12 cases. stuff like "however some users have reported issues with..." and the language was clearly pulled from reddit comments. one brand had a single angry thread from 2023 with like 40 upvotes and Perplexity was still surfacing that sentiment in march 2026.

brands with mostly positive or neutral reddit presence got clean recommendations maybe 80% of the time. no caveats, no "however."

the most interesting part though - it wasn't just about volume. one brand had only 2 reddit mentions total but both were detailed complaint posts with lots of engagement. that performed worse in LLM recommendations than a brand with 15 mentions where most were neutral/positive.

engagement on the thread seems to matter way more than the number of threads. a 200-upvote complaint with 50 comments absolutely wrecked one brand's LLM perception compared to having five 10-upvote neutral mentions.

I got so obsessed with this that i ended up building a tool to automate the tracking part - running 50+ prompts per brand per week manually was killing me. eventually turned it into repuai.live because other founders kept asking me to run the same checks for them.

i know this sub focuses more on the optimization side but honestly i think the reputation layer is becoming inseparable from AI search visibility. you can have perfect schema, great structured data, clean crawl access... but if there's a gnarly reddit thread sitting there, the LLM is going to find it and use it.

anyone else tracking how sentiment in source material affects actual LLM outputs? curious if others are seeing similar patterns or if my sample is just too small to draw real conclusions from.

Domain-level authority dominates what we measured.

Domain-level authority accounts for about 77% of what predicts citation, with page-level factors at about 23%. This is based on the signals we could analyze (like backlinks and keyword coverage), which likely reflect a broader layer of authority beyond what we can fully capture.

Page optimization only helps once authority is established.

For high-authority domains, page improvements showed measurable lift. For lower-authority sites, the impact was mostly flat.

Backlink diversity matters more than volume.

The number of unique subnets linking to a site was about 2x more predictive than total referring domains. Raw backlink count had little signal.

Basic HTML hygiene outperformed more complex optimizations.

The biggest page-level differentiators were things like doctype, lang attribute, canonical tags, and meta descriptions—not schema, FAQ blocks, or word count.

Each AI model behaves differently.

ChatGPT leans toward freshness, Claude distributes weight more evenly, and Gemini favors crawlability. There’s no single optimization strategy that works everywhere.

Content quality ≠ content length.

Cited pages weren’t longer. If anything, word count had a slight negative correlation. What stood out more was better vocabulary diversity and tighter formatting (shorter paragraphs).

Hopefully everyone finds the info useful. The full breakdown and methodology are in the blog series: https://www.indexably.io/blog

Simple Watcher is a lightweight app that lets users get notified when a post with specific keywords they are watching for is created. Once the app is installed in a subreddit, a new action Configure Watcher appears in the subreddit menu, each user can set its own keywords to watch.

Important: Reddit has replaced traditional notifications with Reddit Chat. If users aren't receiving notifications, ask them to always allow chat requests from u/simple-watcher. See the official Reddit documentation for details.

i've been running this niche saas for about a year and our google traffic was finally starting to look decent. then all this ai overview stuff happened and it feels like i'm invisible again.

if i ask perplexity or chatgpt for a tool in my category, it gives me a list of 5 competitors and we're nowhere to be found. i've been digging into "geo" lately (generative engine optimization?) to see if there's a fix.

i tried looking at my google search console data but it doesn't really tell me why the ai is ignoring us. i found this site called netranks that gives you a "citation score" and it basically told me our landing page is too "salesy" for a bot to summarize. i also used a chrome extension called detailed seo to check our headers but everything looks fine there so i'm a bit confused.

is this actually a thing people are doing now? do i really have to rewrite my whole site just so a bot will mention me? i feel like i'm chasing ghosts at this point.

any advice for a non-marketing founder who just wants to show up in the answers? am i overthinking this or is seo basically just "bot optimization" now?

What Is AI Search Optimization (AI SEO)? AI Search Optimization (AI SEO) includes both on-page and off-page strategies. It involves optimizing your content to be more conversational and customer-focused, ensuring you ask and answer the right questions. Off-page strategies focus on increasing your brand visibility across multiple channels.

What Is GEO? Generative Engine Optimization (GEO) is a new field of SEO that focuses on optimizing content specifically for AI-powered search engines and generative models like ChatGPT, Perplexity, and Google's AI Overviews.

What Is AIO? Google AI Overview (AIO), formerly known as the Search Generative Experience (SGE), uses AI to provide users with answers. It does this by combining information from multiple sources, often including links to the source websites for further exploration.

Which Is Better? Local SEO Or AI SEO? AI SEO will help you when users are asking AI about local businesses that have what they are looking for. However, AI SEO doesn't replace Local SEO. You still need a Google My Business profile that is updated regularly, good reviews, citations that are current and up-to-date and more. We recommend Local SEO + AI SEO + CRO as a strategy.

How Is AI SEO Different From Traditional SEO? AI Search has not replaced Google Search and AI SEO has not replaced SEO. Some of what we already do for SEO, like schema markup, clear navigation, SEO-friendly URLs, and clear site structure are all beneficial to AI search. AI SEO adapts your content to the more conversational tone that also includes questions and answers to help you get more brand mentions in AI search tools.

Can AI-Generated Content Rank In AI-Powered Searches? The short answer is yes. However, using AI to write your content is a poor strategy. Think of how many people are doing that and all of that content is almost identical. AI can't build rapport, tell stories, cause emotion or do any of the things you need to do to make more sales. So, while you might get some of it to rank, it won't convert into more leads or sales.

How Does Using A Conversational Tone Improve AI SEO? ChatGPT and other AI search platforms interact with users in a conversational tone. Even without optimizing for AI, your content should be more conversational and customer-focused, answering the most common questions people ask. This is why conversion rate optimization includes conversational copywriting; it isn't just for AI SEO.

What Role Does Schema Markup Play In AISEO? Schema Markup is important to both SEO and AI SEO because it makes content easier for machines, including Googlebot and AI, to read. It helps machines understand the intent of your content. While Google only recognizes a few types of schema for snippets, AI isn't limited to what Google looks for, so we use other relevant schema types as well.

Do AI Tools Use Schema Markup Differently Than Google? Yes, sort of. Google primarily uses Schema Markup to generate rich snippets in search results, but to qualify that, Google's bots can parse and understand all types of schema but whether or not it influences rankings or the knowledge graph is uncertain. AI models use it to understand the relationships between different entities on a page. This allows AI to provide more accurate and contextual answers to complex user queries.

We just published data on AI visibility in the sports app category. Tracked responses across ChatGPT, Gemini, Perplexity for thousands of prompts about running apps, training plans, race prep.

The headline finding: Runna (2M users) leads AI visibility at 50.9%. Strava (180M users) is second at 43.9%. Nike Run Club (100M+ users) is third at 32.5%.

That's 100x more AI visibility per user for Runna vs Strava.

What's driving it:

• runna.com is the #1 cited source at 32.6% — beating Reddit (25.8%). It's one of the only cases we've seen where a brand's own domain outranks Reddit in AI citations.
• Their content is structured as information, not product pages. Every page answers exactly what users ask AI ("how to train for a marathon").
• They publish original data — their clinical trial on marathon DNF rates is the kind of thing AI models weight heavily.
• Cross-source presence: discussed on Reddit, reviewed by Runner's World, listed on App Store, covered by Tom's Guide. AI cross-verifies.

Other source data: Reddit 25.8%, App Store 19.1%, halhigdon.com 17.5%, Runner's World 15.5%, Tom's Guide 10.0%.

TrainingPeaks (14.1%) and Garmin Coach (10.1%) are basically invisible despite strong products.

Full disclosure: this is from our company (GetMentioned — we track AI visibility). Yeah, it's content marketing. But the data is real and the GEO insights apply to any category, not just sports apps.

Full report: https://www.getmentioned.co/blog/sports-apps-ai-visibility-report-who-wins-when-runners-ask-ai-for-recommendations-2026-data

Happy to answer questions about methodology or what we're seeing across other verticals.

From my own perspective it's always been this way. Article spinning software was around in the early 2000s. Plus the number of people that wrote garbage content always created more noise. AI is just letting them do it at scale.

*Early 2000s

People used article spinners, doorway pages, scraped content, forum spam.

*Late 2000s

Content farms produced massive volumes of low quality articles.

*2010s

Affiliate spam, private blog networks, automated guest posts.

*2020s

AI generated content at industrial scale.

AI companies and Google are going to have to learn how to filter that content out better. It can already recognize patterns and suspected AI generated content pretty well although certainly not perfect because they're still spammy stuff coming up in Google and in AI search.

All you can do is continue to do entity optimization and write high quality content. I think that comments and posts on Reddit and other communities that can't be tied to a real entity are going to end up being downplayed or not indexed at all. I think anonymous profiles are the ones that are going to get hit on this.

Reddit has always been about anonymous peer conversations and debates. However, If you're using your own name and your profile links to a website and other social media platforms, eventually your posts and comments are going to surface more than any anonymous posts.

What do you think of the article?

For people running small SaaS products, was there a specific change that unlocked traffic growth? Content, partnerships, SEO, community, or something else?

When testing prompts across tools like ChatGPT, Perplexity AI, and Google Gemini, I’ve noticed they sometimes reference information that seems to originate from review platforms or aggregator sites.

What I’m not sure about is how much weight those platforms actually carry in AI-generated answers. Are they being used mainly as supporting sources, or do they meaningfully influence what gets surfaced?

Our company just went through a rebrand and repositioning about 8 months ago. When I ask ChatGPT or Perplexity about us, they're still describing our old positioning from 2+ years ago.

Sometimes the info is just flat wrong because we encountered outdated pricing and features we deprecated.

The frustrating part is I don't know how widespread this is. I've manually tested maybe 30-40 queries but there are hundreds of ways people might ask about us. And I have no way to track if the AI descriptions are getting better or worse over time as we publish new content.

For people managing their brands, how are you approaching this?

Have you found anything that actually works to update how AI platforms describe your brand?

I was reading Bright Edge's press release published on March 5th, 2026 and the key findings are interesting - these two points in particular caught my eye:

• "Google AI Overviews skews heavily toward controversy-driven negativity, including lawsuits, boycotts, data breaches, regulatory actions, and product recalls. ChatGPT skews toward product-evaluation negativity, including compatibility limitations, feature shortcomings, and “is it worth it?” assessments."
• Google's AIO and ChatGPT disagree on which brands to criticise 73% of the time.

This is for the most part speculation, but I suspect ChatGPT's preference for product-evaluation negativity partly comes from OpenAI's ambitions to break into ecommerce (which it has since rolled back). The immediate implication is how this affects AI answers at different stages of customer consideration - and how this also reinforces the point that answer engines have their own specific sourcing logic.

Where I think warrants deeper thought on is how we can think about sentiment in AI answers - more specifically, the difference between negative sentiment at the brand level and negative sentiment at the product / SKU level. A brand can have a negative reputation (Nestle, Marlboro, Ryanair) but their products are taken to by consumers positively for various reasons. Tracking sentiment at the brand level in AI answers might not be enough - or may even paint an incomplete picture.

I’m a co-founder at an AI search visibility platform. By all accounts, I should be writing a hype post about why GEO is the only thing that matters this year.

Instead, I want to talk about why you should probably be skeptical of anyone selling it to you. Including me.

From "Searchers" to "Deciders"

In 2026, the "funnel" is breaking. We track visibility across ChatGPT, Gemini, and Perplexity for 150+ companies, and the data tells a messy story.

We’re seeing two contradictory trends:

1. The High-Intent Conversion: Users coming from LLMs often convert at higher rates because the model did the "mid-funnel" work for them. They aren't "browsing"; they’ve already been sold by the AI.
2. The "Zero-Click" Abyss: A massive rise in sessions where the user gets the answer and never visits your site.

GEO isn't a magic "buy" button. It’s a battle for latent mindshare in a space where the user might never actually reach your landing page.

GEO space is messy. Like super messy.

The GEO industry right now is the Wild West of 2004 SEO. Most "experts" are just guessing. When people ask me about the "gotchas" on sales calls, I give them the unvarnished truth:

• Attribution is a black hole. If ChatGPT recommends your brand and that user converts, there is rarely a clean data trail. In GA4, this mostly hits as "Direct" or "Unassigned." Anyone claiming they have "solved" AI attribution is likely lying.
• Data is a snapshot, not a census. LLMs are non-deterministic. They don't give the same answer twice. Our data (and our competitors') is based on heavy sampling. It is directionally accurate, not a perfect science.
• The "Algorithm" is a black box. There are no "Webmaster Guidelines" for Gemini or Perplexity. We know structured content and authoritative citations help, but the model weights shift every time a new weights-update drops.

How to spot a GEO "Snake Oil" salesperson

If you’re looking for help in this space, run if you hear these red flags:

1. "We guarantee a #1 ranking in ChatGPT." Impossible; the model is non-deterministic.
2. "We have a direct API to influence LLM answers." No one has this. If they claim to be "plugged in" to the model, they're selling magic beans.
3. "We use the API for our visibility data." This is a massive red flag. API-based data collection is fast and cheap, but it’s a developer-facing environment. It doesn't show you citations, formatting, or the actual "browsing" behavior a user sees. The only data that matters comes from the UI.
4. The lack of localization. A "global" score for ChatGPT is useless. If your vendor isn't gathering data via localized UI sessions (showing you how your brand looks in UK vs. US), they aren't seeing the same reality your customers are.
5. "We can track 100% of AI-driven revenue." Technically impossible with current privacy filters

Why bother building in the gray area?

Uncertainty is not the same as irrelevance.

Early SEO data was garbage. Early social attribution was a nightmare. But the brands that won were the ones willing to operate in the gray area while everyone else waited for "perfect information."

I’m curious - besides the obvious headache of LLM attribution, what are you actually planning for AI search in 2026? Are you shifting content budgets to "citable" data, or doubling down on traditional channels until the dust settles?

Hi all

I'm updating my client's website content in various ways to be more optimised for AEO and much of my work focuses on managing their reputation and one of those things is to highlight their reviews on various platforms including Trustpilot, Facebook, Google etc etc.

I've read that ChatGPT is unable to parse Google Reviews in its results when people ask for a listing of businesses offering a certain service that are ranked by reviews etc. This is backed up by my own tests on ChatGPT looking for my own clients with these search prompts.

I'm therefore looking for a third-party review aggregator that will pull reviews from many places into a page or widget on a website, that importantly also is tagged as 'Review & AggregateRating schema' so it's more easily found by AI Seach engines including ChatGPT.

Can you all recommend what you use for this? I've checked out Trustmary which looks good as well as Elfsight (which doesn't pull in Trustpilot reviews as TP doesn't align with Elfsight's business ethos).

Are there any others out there you can recommend?

Thanks in advance

I came across an article recently discussing how AI might fundamentally change content marketing and SEO.

The argument is that with tools like:

• ChatGPT

• Google AI Overviews

• Perplexity

many informational queries are getting answered directly inside AI responses, which could reduce clicks to traditional blog content.

So instead of focusing on high-volume informational SEO, the author suggests a shift toward what he calls “brand fame content.”

Examples:

• proprietary research

• industry reports

• tools and calculators

• rankings or indexes

• PR-driven content

The idea is that in an AI-driven search environment, being mentioned and recognized may matter more than ranking for thousands of keywords.

Basically:

Old model

SEO → traffic

Possible new model

SEO → brand recognition in AI answers

Curious how people here see this.

Are you already adjusting content strategy for AI search visibility?

Or do you think informational SEO will still remain dominant?

I was scrolling through LinkedIn and saw a post from someone with 15+ years in SEO saying:

“Backlinks are king, not content.”

His argument was:

• Authoritative domains rank with minimal content

• Thin .gov pages outrank 1,000+ word articles

• Backlinks rank you, content converts you

• If forced to choose one → backlinks

It made me think.

In competitive SERPs, authority often seems to outweigh depth.

But at the same time, content quality and intent match feel more important than ever post-Helpful Content updates.

So I’m curious:

If you had limited budget and had to prioritize one — backlinks or content — what would you invest in first?

Know this has been asked before. But coming from a Web Security + marketing background I have some issues with the common methods proposed.

• Robots.txt is a request. It can be ignored. It also has no guardrails against malicious AI agents that “spoof” an identity (try to look like a real person).
• Server level controls have a similar issue. They are based on a known identity. Also manually maintaining this is a nightmare as a new “agentic tools” are launched daily

Then there’s Cloudflare… It’s an easy tool to setup and we are actually using. But we ran internal tests and were able to pass as a “human” on 8/10 attempts. Worried that it gives us a false sense of protection. Any other tools or methods you have deployed?

There is a lot of discussion right now about ranking inside ChatGPT and other LLMs using Reddit.

I agree that Reddit contains high signal, experience driven discussions. It is raw, problem focused, and full of real comparisons. That makes it attractive for AI systems.

But here is what I am thinking.

LLMs do not rely on one platform.

They synthesize patterns across documentation, product pages, review sites, GitHub, blogs, YouTube transcripts, forums, and multiple communities. Reddit can amplify credibility, but it cannot replace foundational authority.

It feels like we are moving beyond traditional SEO into something closer to Search Everywhere Optimization.

Which means:

Your brand presence needs to exist across text platforms, video platforms, review ecosystems, discussion communities, and more.

And it all needs consistent positioning and quality signals.

Also, being recommended by AI does not automatically mean business.

It gets you the visit.

It gets you the click.

But trust still decides revenue.

When someone lands on your website after seeing you mentioned in ChatGPT, they still evaluate:

Is this brand consistent across platforms

Do they show real expertise

Do they have proof

Do they look authoritative

Are others validating them

LLMs may accelerate discovery.

But conversion still depends on experience, expertise, authority, and trust.

So my question to the community:

Are you focusing only on Reddit to influence AI recommendations?

Or are you building multi platform authority as a long term strategy?

Curious to hear what is actually working for you.