r/GenerativeSEOstrategy

Our SEO is in a good place but our visibility in ChatGPT and Google AI Overviews is still low.

That has me looking into GEO and AEO but most agencies seem to make big promises without much proof.

I definitely think AI search is becoming more important and I don’t want to ignore it and fall behind.

Has anyone found a trustworthy agency or a solid framework for improving AI search visibility?

Would love to hear what’s working for other local businesses.

Google’s new “Intelligent Search Box” feels like a shift from keyword-based SEO to intent-based SEO.

If users start searching conversationally with prompts, images, and videos instead of short keywords - will exact-match keywords matter less now?

Curious how everyone sees this changing SEO strategies.

I’ve been collecting insights from SaaS founders, CMOs, growth marketers, and content leads about the SEO/content/AI visibility challenges they’re trying to solve this year.

The pattern was pretty clear:

A lot of SaaS teams are no longer focusing on ranking keywords.

They’re asking things like:

• How do we get mentioned in ChatGPT, Perplexity, and AI Overviews?
• How do we turn organic traffic into demos, trials, or signups?
• How do we create product-led content that actually helps buyers decide?
• How do we prove SEO ROI when more discovery happens without a click?
• How do we compete with bigger SaaS brands that already dominate search and AI answers?
• How do we scale content with a small team without publishing generic posts?

One thing that stood out to me is that many teams still have traffic, but they’re struggling with conversion or visibility in AI-assisted research. Some are ranking on Google, but not showing up when buyers ask AI tools for recommendations. Others are getting visits, but the content doesn’t clearly connect the problem to the product.

It feels like SaaS SEO is becoming less about “publish more content” and more about building a system:

intent → content → product clarity → proof → conversion path → authority → AI visibility

The biggest shift, in my opinion, is that generic content is losing value fast. AI can summarize basic informational content easily. What seems to be working better is specific content with real examples, comparisons, product context, customer proof, use cases, and clearer answers.

Curious what others are seeing.

If you work in SaaS, what’s the biggest SEO, content, or AI visibility challenge you’re trying to solve this year?

I’m building my website right now and only have the landing page done so I feel like this is the perfect time to make every page as SEO friendly as possible from the start.

So far I’ve done keyword research and a content gap analysis with the top-ranking competitors. Based on that I have a list of topics I think I should cover.

Does that sound like the right approach?

I’d also love to show up in AI search results, but traditional SEO is my main priority for now.

If you could give one prompt or framework for writing really strong SEO content, what would it be? Same question for GEO.

Also what blogs, courses, newsletters, or other resources have actually helped you learn what works?

Is anyone else worried that AI search is going to make it much harder for newer websites to break through?

With traditional SEO, even smaller sites had a path. You could target long-tail keywords, slowly build authority, and eventually compete. But AI answers seem to compress visibility down to just a handful of cited sources.

What concerns me is that once certain brands become recurring AI references, they may just keep reinforcing themselves because users stop clicking around and discovering alternatives. Feels like the feedback loop could get very concentrated very quickly.

Will AI search actually create new opportunities for niche sites or mostly strengthen whoever already has the most visibility and mentions online?

I keep noticing smaller brands showing up in AI answers even when their actual sites are not that strong from a traditional SEO perspective. No huge backlink profile, not dominating rankings, nothing that would normally make you think they would get picked up so often.

But then you look closer and their name is everywhere. Reddit threads, niche communities, blog mentions, random discussions, people casually recommending them in comments. It feels like AI tools pay a lot of attention to repeated presence across the web, not just who has the best optimized page.

Lately I have been wondering if distribution and brand mentions are becoming just as important as on page SEO for GEO. Curious what others are seeing. Are you spending more time trying to get your brand talked about in different places now, or still mostly focused on improving content on your own site?

I keep seeing people talk about GEO like it’s this totally new thing, but so far it feels like regular SEO with a different label.

We’re still focusing on solid content, clear structure, and answering search intent, and that seems to work fine whether someone finds us through Google or AI tools.

Is anyone here doing something specifically for AI search engines, or are you basically sticking to the same SEO strategy?

Just trying to figure out if GEO is a real shift or mostly marketing buzz.

I’ve got a couple of interviews lined up with some Big style agencies and enterprise brands. Looking at the JDs, I’m seeing a massive shift. It's no longer just about 'organic growth'; they’re asking for 'Citations in LLMs' and 'Generative Engine Visibility.'

For those who have interviewed for Senior or Director roles recently—how deep are they going into AI optimization (GEO)? Are they asking for specific case studies on how you got a brand cited in ChatGPT/Gemini, or are they still mostly focused on the traditional 'Core Web Vitals + Content Clusters' stack?

I want to know what I actually need to update in my 'expert' toolkit before I walk in.

Isn't it strange that some of our pages barely rank in Google, have almost no backlinks and still end up showing up inside AI answers pretty consistently.

Meanwhile, pages we spent way more time optimizing for SEO stay completely invisible in tools like ChatGPT or Perplexity.

For those testing GEO seriously, what are you focusing on right now? Cleaner structure, stronger wording, easier to quote answers?

Can you tell me how do you do AEO/GEO now for your brands? I am a digital marketing person, and my boss asking me about this. Are you mostly a digital marketer, business owners or SEO or agency? My questions:

• How are you actually doing it?
• Are you manually checking ChatGPT/Gemini prompts?
• Are you using any AEO/GEO tools yet?
• If yes, which ones and are they actually useful?

Thanks guys! Much appreciated.

Been quietly working on answer engine optimization for a Series B agritech SaaS over the last several months. Started as an internal audit, turned into a full implementation. Sharing everything I've mapped so far i.e. what worked, what's still unclear, and what I'm genuinely curious about from people further along than me.

This is long. Worth it if you're actively building in this space.

A- The starting point viz. why Google rank means nothing for AI visibility

The company I was auditing ranked well on Google across their core category keywords. Decent traffic, solid backlink profile, clean technical SEO. Completely invisible in ChatGPT, Perplexity, and Gemini when buyers searched the same queries.

The gap isn't an anomaly. It's structural. Google rewards topical authority and backlink equity. LLMs reward entity clarity and off-site corroboration. Different trust signals entirely. A brand can rank #1 on Google and still be absent from every AI-generated answer in their category.

B - The On-site extraction architecture

This is where most AEO advice starts and stops. It's necessary but not sufficient on its own.

What actually moves citation probability on-site:

Research consistently shows ~44% of LLM citations pull from the first 30% of a page's content. Front-loading matters more than comprehensive coverage. Lead every section with the answer, follow with the proof. Never the reverse.

Headers should mirror exact prompts your ICP types into AI tools, not keyword targets, not some clever copy. If your buyer asks ChatGPT "how does X solve Y problem" your H2 should read exactly that way.

FAQ sections bolted at the bottom of pages don't work. The FAQ logic needs to be embedded in the body content structure itself. Each section functioning as an implicit question and answer.

JSON-LD schema for FAQPage, HowTo, and Article types genuinely helps. Not because Google cares, yet because it gives the LLM a pre-parsed semantic structure to extract from without ambiguity. Organisation schema with consistent NAP data across every page builds entity clarity.

llms.txt is worth implementing. Analogous to robots.txt but specifically for LLM crawlers lets you signal which content is highest priority for extraction. Still early but adoption is growing fast enough that ignoring it is a mistake.

robots.txt make sure you're not accidentally blocking AI crawlers. GPTBot, ClaudeBot, PerplexityBot all need explicit allowance if you've been aggressive with your crawl restrictions.

C- Off-site entity footprint

This is where most brands have the biggest gap and where the audit for the Series B company was most revealing.

LLMs triangulate brand legitimacy from independent third-party sources. If you only exist on your own domain, the model has nothing to corroborate you with and won't cite you confidently regardless of how well-structured your pages are.

What the citation environment mapping revealed: their competitors were being cited primarily through four source types viz. G2 and Capterra reviews, Reddit threads in niche subreddits, Crunchbase and Tracxn profiles, and long-form technical content on Medium. The company had minimal presence across all four.

The priority fix order matters by category. For B2B SaaS: review platforms first (G2, Capterra brands with active review profiles have meaningfully higher AI citation rates), structured entity data second (Crunchbase, Wikipedia if notability threshold is met), community presence third, long-form editorial fourth.

For developer tools the order shifts: GitHub presence and Stack Overflow tags move to the top. LLMs answering developer queries pull heavily from these sources.

Review velocity is a real signal. A detailed user review that says "this tool solved our API routing issues in three days" functions as a direct citable answer to a future prompt from someone asking Perplexity for API routing solutions. The review isn't just social proof it'll be your citation infrastructure.

D-Citation environment mapping

Before optimising anything, map what's actually getting cited in your category.

Run your target queries through ChatGPT and Perplexity. Don't search your brand, instead go on to search your category. Screenshot every cited source. Categorise them by source type. This gap map tells you exactly what you need to build, in what order, rather than optimising blindly.

The brands consistently winning citations in most B2B categories share a pattern: they have presence across at least 4-5 independent source types simultaneously. Single-source brands i.e. even with excellent on-site content, sometimes get bypassed.

E- What I've been experimenting with 'CBaaS architecture'

One thing I've been testing is building what I'd call Citation Banks viz. single content nodes engineered to satisfy multiple related query intents simultaneously rather than one piece per keyword.

The logic: LLMs extract semantic tokens rather than reading pages linearly. A single well-architected piece can serve as the cited source for an entire family of related queries. Every successful retrieval strengthens that node's citation weight for adjacent queries. The compounding is real, a piece that answers 6 related intents accumulates citation weight 6x faster than 6 separate pieces each answering one.

Construction requirements are different from standard SEO content. Query clustering before writing via mapping the entire family of intents you're targeting. Semantic bridges between sections that help the LLM understand the relationship between adjacent questions. Front-loading the most critical answers in the first 30% of the document.

Still testing this properly but early signals from the Series B implementation are promising.

F- The founder content signal, something I wasn't expecting

One finding that surprised me: founder-authored content was getting cited significantly more than equivalent brand content across the same topics.

The mechanism makes sense in retrospect. LLMs are trained heavily on conversational, human-written content i.e. Reddit threads, technical blogs, build-in-public posts. Authentic founder voice pattern-matches to this training data. Polished brand copy pattern-matches to advertising, which retrieval systems treat with skepticism.

A founder documenting a real product decision including the wrong assumptions, the pivot, the failed experiment, creates context-rich content that retrieval systems extract for "how-to" and "best practice" queries. The specificity and honesty is what makes it retrievable, not the polish.

Have been calling this Thought Leadership as a Service internally viz. the systematic documentation of a founder's building journey structured specifically for LLM extraction rather than human readers. Shifting thought leadership from PR vanity metric to data infrastructure.

G-Entity velocity, what I still don't fully understand

This is where I have observations but incomplete theory and would genuinely value input from people further along.

Entity velocity seems to matter, the rate at which new citation signals are accumulating across independent sources. A brand that optimised six months ago and went quiet appears to lose citation ground over time even if their content hasn't changed. The retrieval system seems to interpret signal stagnation as entity decay.

What I can't fully quantify: the half-life of a citation signal. How quickly does a G2 review or a Reddit thread decay in citation weight? Is it uniform across source types or does it vary? Does a Wikipedia entry decay more slowly than a Reddit thread because of perceived permanence?

If anyone has data or even directional observations on this I'd genuinely find it useful.

What's still unclear to me

A few things I haven't been able to resolve cleanly:

How much does llms.txt adoption actually move citation probability right now versus six months from now as crawler support matures?

For brands in genuinely niche B2B categories with low Reddit presence, is community seeding the right answer or is there a faster path to off-site entity corroboration?

Does author entity weight on Medium (follower count, publication history, external links to author profile) affect citation probability of articles published there? Or is it purely content and domain level signals?

How do retrieval systems handle contradictory information across sources — if your G2 reviews describe your product differently from your homepage, does that create citation ambiguity or does one source type override?

Happy to share more

Lately I’ve been seeing certain pages get picked up in ChatGPT and Perplexity responses even though they barely show up in Google search results. Makes me wonder if AI tools are evaluating content on a completely different set of signals.

Feels like direct, answer-focused pages might be getting more visibility now. Or maybe things like structure, entity clarity, and proper citations are starting to matter more than backlinks and rankings. Curious if anyone else has noticed the same pattern.

I’ve been thinking about this because Google is now removing FAQ rich results from Search, so the old SEO reason for adding FAQs is basically gone. For a long time, a lot of sites used FAQs because they could get those expandable dropdowns in the SERP. More space, more visibility, maybe better CTR.

But if that’s the only reason someone was adding FAQs, then yeah, that tactic is probably dead. That said, I don’t think FAQs themselves are dead. I think the purpose has just changed. For SaaS and B2B sites especially, FAQs still help because they answer the questions buyers usually have before they convert. Things like:

• Is this actually for a company like ours?
• How is this different from other options?
• What happens after signup or after booking a call?
• What results should we realistically expect?
• How does pricing work?
• Do we need this now, or is it too early?

Those questions may not always fit naturally in the main page copy, but they matter a lot for conversion. They also help with SEO in a different way. Not because of the rich result anymore, but because they help cover long-tail questions, objections, comparison points, and intent that users actually search for. I also think FAQs are becoming more useful for AI visibility, but not in a “just add FAQ schema and AI will cite you” way. That feels too simplistic. The real value is that FAQs create clean, direct Q&A blocks that are easier for both users and machines to understand. Clear questions, specific answers, and better context around what the page is actually about.

So I wouldn’t delete FAQs just because Google removed the SERP feature.

I’d audit them.

If the FAQ section is generic, duplicated, or only there for schema, it probably needs to go or be rewritten. But if it answers real buyer questions and makes the page clearer, I’d keep it.

AQs are still worth doing, but lazy FAQ sections are not.

The old goal was SERP real estate. The new goal is clarity, intent coverage, buyer education, and making your content easier to understand.

Curious how others are handling this. Are you removing FAQ schema, keeping it, or just rewriting FAQs so they serve the page better?

Not sure if everyone caught this, but when Reddit sued Perplexity back in October, the drop was insane.

Perplexity’s Reddit citations apparently fell off a cliff, down 86% almost overnight. And to fill the gap, YouTube citations just shot up. It made me realize how fragile this whole "AI visibility" thing is.

We’ve been spending a lot of time trying to get our brand mentioned in Reddit threads, hoping the AI tools would pick it up. But if one lawsuit or corporate dispute can just wipe out an entire channel overnight, it feels incredibly risky to over-index on one platform.

We're starting to diversify our strategy now, looking at YouTube, niche forums, and some other communities. Just a reminder that relying on a single platform for your AI visibility is kinda dangerous. Anyone else pivoting their strategy because of this?

We looked at 10.4M clicks and 54M impressions across 419 Quebec-based SME websites over 16 months, then compared the current post-AI Overviews click distribution with pre-AIO CTR benchmarks.

The main pattern was not “SEO is dead”.

It was that organic clicks are getting much more concentrated.

Positions 4-10 lost around 70% of their click share compared to pre-AIO benchmarks.

That means they went from capturing around 30-45% of page-one clicks to 10.8% (post-AIO).

Barely 1 out of 10 clicks.

The pattern was pretty blunt:

- The Top 3 captured 89.2% of all page-one organic clicks
- Position #1 alone captured 63.6%
- Position #7 averaged a 2.6% CTR
- Positions 4-10 captured 10.8% of page-one clicks, compared to around 30-45% before AI Overviews

So users still click organic results.

They just seem to click much less deeply into the page.

That makes “ranking on page one” feel like a weaker metric than it used to be. If the ranking sits below the first few results and barely drives traffic, the strategic question changes.

It’s not only “how do we rank?”

It’s also “where else can this brand become visible, trusted and cited?”

Curious how others are adapting their SEO/GEO prioritization.

When a keyword seems capped around positions 4-8, do you keep pushing for the Top 3, or move effort toward long-tail keywords, AI citations, third-party mentions or brand demand?

And what signals do you use to decide when classic ranking work is still worth the effort?

They are the same thing. Different names for the same objective: optimise a brand's presence in an output. Whether that output is a search result, an AI citation, or a generative summary, the metric is the same. Did the brand appear?

Appearance is not selection.

Agentic Brand Control is a different category with a different objective entirely.

When an AI agent runs a buying conversation on behalf of a consumer - assembling a consideration set, evaluating criteria, eliminating options, and routing to a final recommendation - the question is not whether your brand showed up. The question is whether it survived.

We call the final recommendation the T4 handoff. It's the moment a brand either takes the sale or disappears from the journey. In 12,000+ buying sequences we've run across ChatGPT, Gemini and Perplexity, 87% of brands that appear early don't reach it.

The gaps that determine survival are diagnosable. Entity recognition. Criteria alignment. Price justification. These are not content problems. They are evidence problems — specific, structural deficits in how an LLM interprets a brand when it has to make a decision under open consideration.

That is what Agentic Brand Control addresses. Not visibility. Selection.

The objective is to close the gap between a brand appearing in AI outputs and a brand being chosen at the end of the conversation that matters.

The category is new. The measurement is real. The stakes are rising.

Are you an SEO, a GEO/AEO or an Agentic Brand Controller?

I've noticed AI search just spits out like 2 or 3 recommendations instead of a whole page of results.

How are new companies supposed to get found now? With normal SEO you could rank for long-tail keywords and slowly build traffic. But if AI just summarizes everything into a tiny list, aren't the same giant brands just going to hog all the visibility?

I guess the only play is being hyper-niche. Like, staying in a small "puddle" where your positioning is so clear that AI has to mention you. But honestly? It feels like the walls are closing in for new businesses.

Do y'all think it’s just going to favor the mega-brands from here on out?

A recent analysis reviewed 12,487 AI-generated answers about flower delivery brands for Mother's Day across six AI models to find out which sources appeared most frequently in AI recommendations.

One finding stood out: 3 of the 5 most cited social media/user-generated content were old Reddit threads.

Not a few months old.

One cited Reddit thread was 4 years old. One was 6 years old. Another was 9 years old.

The most interesting example was the 6-year-old Reddit thread: a negative post about major flower delivery brands acting as middlemen. It had 13,000 upvotes and 203 comments, and was the #2 most cited social media/user-generated content.

I’ve never seen AI cite content this old for any type of query.

What do you make of this?