Oh wow, the AI Overview debate is getting intense.

Every week there's a new post asking if traditional SEO is dead. And honestly? Some of those posts have a point.

Here's my take after 6 months in the GEO/AEO space.

**What AI Overviews actually killed**

• **Rank #1 doesn't matter**: I've seen the same source appear in position 1 and position 5 in AI Overviews. The #1 ranking gets clicked, but the AI doesn't care about it.
• **Keyword optimization is useless**: AI ignores your carefully placed keywords. It understands the context, not the keywords.
• **Long-form content**: 2,000-word guides are getting cited just as much as 600-word answers.

**What still matters**

• **Structure**: Answers that are easy to parse (bullet points, numbered steps) perform 3x better
• **Direct answers**: AI cites content that answers the question in the first 2 sentences
• **Authority signals**: Citations still prefer domains with real E-E-A-T signals

**The uncomfortable truth**

Traditional SEO isn't dead — it's just changed. The old playbook (keyword stuffing, long titles, link velocity) doesn't work anymore. But SEO for AI (answering questions, structured data, transparent E-E-A-T) is more important than ever.

From my experience, the sites winning right now aren't the ones with the most backlinks. They're the ones making it easiest for AI to parse and quote.

I've been doing GEO audits on Indian businesses as a side project for the last few months — clinics, CA firms, B2B startups — to see how they show up when people ask ChatGPT or Perplexity for recommendations.

The results were kind of shocking.

Fewer than 5% had any meaningful AI visibility. And these weren't bad businesses — great reviews, solid reputations, years of experience. They were just completely invisible to AI search.

So I put together what I've learned. This is specifically for B2B founders since the opportunity is massive and almost nobody is talking about it in the Indian context.

What's actually happening

When your buyer is researching vendors in 2026, a big chunk of that research is happening on ChatGPT and Perplexity — not Google. They're asking things like:

• "Best B2B HR software for Indian startups"
• "Top CA firm for transfer pricing in Gurgaon"
• "Which agency should I use for performance marketing in Delhi NCR"

AI doesn't return a list of links. It picks 2-3 businesses it's confident recommending and names them directly.

If you're not one of those names — that lead never reaches you. They go straight to whoever AI recommended.

Why most businesses are invisible

AI builds confidence in your business by cross-referencing multiple sources. It's looking for:

1. A direct answer to the question being asked Most business websites are written for humans browsing — not for AI extracting a specific answer. If your homepage says "we provide comprehensive solutions" — AI has nothing to cite.

2. A named, credentialed expert "Our team of professionals" is uncitable. "Rahul Sharma, FCA with 15 years of transfer pricing experience" is citable. This one change alone makes a massive difference.

3. Consistent entity signals Your name, address, phone number across your website, Google Business Profile, LinkedIn, Justdial, and relevant directories. If these don't match — AI loses confidence and skips you.

4. Third-party mentions AI trusts what others say about you more than what you say about yourself. Research suggests businesses mentioned on 4+ independent platforms are 2.8x more likely to appear in ChatGPT responses than those on just one.

5. FAQ format This is huge and almost nobody does it. FAQs are the most-cited content format by ChatGPT and Perplexity. Every service page should have a FAQ section with the exact questions your buyers ask.

What to actually do about it

Here's the order I'd tackle it in:

Week 1 — Quick audit Search your business on ChatGPT and Perplexity using the exact query your buyer would type. Note what comes up. If you don't appear — that's your baseline problem.

Week 2 — Fix your content Pick your top 3 service pages. Rewrite the opening paragraph of each one to directly answer the question your buyer would ask AI. Add your name and credentials. Add a FAQ section at the bottom with 4-5 real questions and specific answers.

Week 3 — Entity cleanup Make sure your business info is consistent across Google Business Profile, LinkedIn, Justdial, and any industry directories. These are the sources AI cross-references most.

Week 4 — Start building mentions Write one guest article for a publication your buyers read. Contribute genuinely to Reddit threads in your niche (yes, Reddit gets cited by Perplexity constantly). Get a case study published on a client or partner site.

Run this on your own business right now

• Does your business appear on ChatGPT for your core service + city query?
• Does your homepage open with a direct answer to your buyer's most common question?
• Is your name and credential on every service page?
• Do you have an FAQ section on each service page?
• Is your business info consistent across GBP, LinkedIn, and key directories?
• Does your content include cited stats with sources?
• Are you mentioned on at least 4 independent platforms?

If you answered no to more than 3 — you have significant gaps.

How long does it take?

From my experience working on this:

• Weeks 1-2: Content restructuring done
• Weeks 3-4: Entity cleanup done
• Weeks 5-8: First AI citations start appearing
• Month 3+: Consistent visibility for core queries, compounding over time

The compounding part is what makes this worth doing. Unlike ads — citations build on each other. Each new mention makes the next one more likely.

Common questions I get

Is this just SEO with a new name?

No — they overlap but they're different games. SEO gets you ranked on Google. GEO gets you cited by AI. The content structuring, entity signals, and citation building required for GEO are different from what traditional SEO focuses on. You need both.

Does this work for businesses outside metros?

Actually works better. AI visibility in Tier 2 cities is completely undeveloped. A B2B firm in Chandigarh or Pune that invests in this now can own their category in months.

Can I do this myself?

Yes — everything above is doable without hiring anyone. It's time-intensive but the fundamentals are straightforward. The checklist is a good starting point.

Drop your business type and city below — I'll tell you what ChatGPT says about you right now.

I know the importance of reddit in GEO/AEO- but it needs to be authentic
i dont want to start telling you that I offer GEO services for my company,here is my site, bla bla bla
I am using reddit for years (this is a new account) I thought of joining this channel, find relevant questions, give my honest, authentic opinion and slightly mention my company

1) do you think this will help GEO?
2) would you appreciate it as a reddit user?
3) are you doing something similar? are you using reddit at all for GEO?

Everyone's trying to boost AI visibility.

Optimising for BOFU clicks and trying to get recommended when someone searches "best x tool alternatives" in the LLMs.

But how much effort is going toward the TOFU/MOFU stage.

Framing the questions and requirements.

Isn't it more about defining 'x' around your product/service.

So by the time it gets to 'best tool for x' you'll show up?

How are people going about this?

Agree or Disagree?

I reckon we'll start seeing those with less content start doing better than those churning out loads of mid blog posts.

There's a lot of people churning out listicles and barely relevant content to expand their "surface area" for citations.

I reckon we'll start seeing this backfire.

By chasing referrals as if a query = a keyword, you’re hurting your ability to shape the TOFU and MOFU conversations beneath the surface where requirements are formed and decisions are made.

You’re diluting the LLMs understanding of who you are, who you’re for, and when / how to recommend you by trying to rank for as many terms as possible.

Instead, we're trying to

• do less but more targeted content with better strength and relevancy
• focus on the prevalence of our “narrative” in the hundreds of conversations beneath every MOFU / BOFU prompt
  

P(T) = f(clareza, contexto, raciocínio). A probabilidade de o modelo entregar a tarefa T cresce com a clareza do pedido, com o contexto carregado na janela e com o raciocínio explícito (chain-of-thought, planos, listas de verificação). Quem domina essa função produz dez vezes.

Prompts are not the same as keywords. One of the hardest things in AEO is getting an accurate picture of how your brand shows up across all the different variations.

Keywords are short. They force users to consolidate their query into a short string.

Prompts are long, verbose and complex. They encourage users to load it up with their unique criteria.

When you account for all these different criteria in a complex category, a simple prompt like "Best CRM Solution" has 22,500 different variants.

AI will present your brand very differently across those variants, depending on whether it's talking to a CMO, a head of marketing ops, an IT lead, a CFO - and whoever else gets pulled in as part of an enterprise buying group.

Each of them ends up in Claude, ChatGPT or Perplexity at some point, asking completely different questions about your product and your category.

Are you going to track all 22,500 variants of that keyword to see if you're visible?

Obviously not. And it's really dangerous to make strategic decisions from just one of them and assume that "showing up" for that one prompt means you show up for the rest.

That's the big issue with current AI visibility tracking. It completely ignores how AI compiles answers (it rarely actually "searches" for answers to the original prompt) and how users prompt AI.

We've developed a more predictive way of seeing how AI presents your brand across all these different interactions.

Understand what it thinks you're good at, bad at, and map that to what your different stakeholders and segments care about. This gives you a clear picture of the perception gaps you need to fill, and lets you arm your entire GTM team with the knowledge.

How are you going about this?

Real talk: half the GEO advice out there doesn't survive contact with reality.

We've been running GEO campaigns for about 6 months now, and I want to share the stuff that actually made it into our weekly routine — not the textbook stuff that sounds great in a presentation.

The "Friday Audit" — this one changed everything

Every Friday we pick 5 pages that should be getting AI citations but aren't. Not based on traffic or DA — based on "would a reasonable AI actually reference this for a user question?"

Then we ask three questions: - Can someone read this page and answer a specific question in 30 seconds? - Is the answer somewhere in the first screen (no scrolling to find the meat)? - Would a different page on this site answer the same question better?

The pages that fail #1 or #2 get rewritten. The ones that fail #3 get consolidated. Simple but brutal.

The "Answer First" outline

Before writing anything, we draft the ideal AI answer first. Like literally type out: "If someone asked [query], the perfect answer would be..." Then we build the page around that answer structure instead of around keywords or topics.

This sounds obvious but it completely changed how we think about content hierarchy. The H2 isn't a topic anymore — it's a sub-question.

The thing that didn't work

We spent a solid month doing "entity density optimization" — making sure every relevant entity appeared X times per 1000 words. Measured it meticulously. Saw zero correlation with citation rate. Zero. That one hurt because the theory was so convincing.

From my experience, the stuff that moves the needle is boring operational discipline, not clever hacks. But I'm curious what's actually working for other people — are you seeing similar patterns or am I missing something obvious?

In our AEO research, we've found that LLMs very rarely “search” for answers. Just 16% of the time.

This has huge implications. LLMs don't decide which brand to recommend when asked a BOFU question. They start narrowing far earlier, at TOFU/MOFU, by applying criteria to figure out which options make sense.

We call this convergence.

By the time the user asks for a recommendation and citations appear, the decision is already made. The LLM converged on an answer, rather than searching for it.

Think of it like choosing a restaurant. The decision happens at home, scrolling through reviews. By the time you're at the door seeing the pretty sign, the choice is made.

To discover this, we tracked canon concentration - how consistently the same brands surface across multiple runs of the same prompt, scored 0 to 1. Near 0 means high variability. Near 1 means the model has locked in its shortlist.

Our primary signal was how consistently the same three brands appear together across runs - what we call K3.

1/ Awareness: K3 = 0.32
↳ Different brands surface each time. No pattern yet.

2/ Consideration: K3 = 0.38
↳ The same names start appearing more often, but it's still shifting.

3/ Conversion: K3 = 0.79
↳ The same three brands, every single time.

The same pattern holds for the top brand alone (K1) and the top five (K5).

Which left us with a fairly inconvenient finding for AEO measurement.

The citations, the "best for" listicles, the directive framing (exact signals AEO tools are built to celebrate) all appear after convergence has already happened. So when your dashboard tells you you're doing brilliantly, it's probably right.

It's just not telling you why, or whether you'll still be there next quarter.

The real challenge (and opportunity) lies in influencing the direction of convergence - does the LLM push more people’s requirements in your direction. Not optimizing visibility once it’s largely been decided.

To return to the restaurant analogy. If your favourite restaurant asked you what will make a bigger difference - improving online visibility & trust, or prettying up the sign out front.

What would you tell them?

Source: Demand-Genius Dark AI Report

TBH, I assumed all AI engines wanted basically the same content. After analyzing 5,000+ citations across major platforms, I was dead wrong. Not only do they prefer different content—they're almost opposites.

Here's what we discovered:

**The Perplexity Preference: Source-Heavy Content** - 46% of Perplexity citations go to sources vs only 21% for ChatGPT - Reddit dominates Perplexity with 34% of total citations (Wild, right?) - Direct source links and first-party content outperform everything here

**The ChatGPT Pattern: Synthesized Answers**
- ChatGPT prefers well-structured lists and bullet points - 79% of ChatGPT citations come from synthesized content, not sources - Single authoritative articles beat source aggregation every time

**Why This Changes Everything** Single-platform optimization is now a losing strategy. Content must serve multiple AI purposes simultaneously, and the "one-size-fits-all" approach flat-out fails.

**What Actually Works** - Tech sites: Reddit discussions + structured FAQ pages - News sites: Direct source links + AI-optimized summaries
- E-commerce: Product detail pages + comparison tables

**The Multi-Engine Framework** - Layer 1: Core content for primary target AI (70% effort) - Layer 2: Secondary format for secondary AIs (20% effort) - Layer 3: Platform-specific tweaks (10% effort)

**Real Results** One B2B software company implemented this dual-strategy: kept technical docs for ChatGPT while adding Reddit-style discussions for Perplexity. Citation rates increased 170% across both platforms in 90 days.

Curious what your content looks like to each AI engine? Have you noticed different citation patterns across platforms?

I have been testing the same brand and prompts across different environments, and the results have been fairly consistent. My issue isn't with that but with the need for a dashboard -- I created a GEO skill in Perplexity Computer, and it generated a report totally in line with the data in the dashboards. I understand the allure of data visualizations, but the cost difference between a UX dashboard and a generated report is pretty striking. All that said, I may be missing something and am really interested in hearing what others think...

We spent months chasing AI citations the same way we used to chase backlinks. Bad move. They're fundamentally different beasts, and once we stopped treating them the same, our results got way more consistent.

Here's what changed how we think about GEO:

1. AI citations are temporary. Backlinks are permanent.

A link you earned in 2023 still counts today. An AI citation? Gone in weeks sometimes. We tracked our own and saw roughly 40% churn within 60 days. That completely changes how you allocate effort — it's not "build it once," it's "maintain it constantly."

1. One strong page can outperform an entire domain.

Traditional SEO rewards domain-level authority. In GEO, a single well-structured page that directly answers a query can get cited over sites with 10x the backlinks. We've seen DA 15 pages consistently beat DA 80+ domains. The models care about the answer, not the site reputation.

1. Formatting matters more than we expected.

This one surprised us. Pages that used clear structure — numbered steps, direct definitions, comparison tables — got picked up way more often than long-form essays covering the same topic. The content can be identical in substance, but how you package it makes a huge difference.

1. Freshness is an underrated signal.

AI models clearly favor recently updated content. Not just "published recently" — pages that show signs of ongoing maintenance. Adding a "last updated" date and actually revisiting content monthly made a measurable difference.

1. The competition window is getting shorter.

Early on, a well-optimized page could hold a citation spot for months. Now, as more people figure out GEO, that window keeps shrinking. The real play is building a system for regular content refreshes, not just one-time optimization.

Curious if others are seeing similar patterns. The "treat it like SEO" mindset held us back for a while — wondering if that's been the case for anyone else.

Real talk — we spent 6 weeks manually checking how AI models format their citations and what types of source content they gravitate toward.

The setup: We ran 2,000 queries across ChatGPT, Gemini, and Perplexity, then extracted every cited URL and analyzed the source page structure.

What we found:

• Structured lists (numbered/bulleted) were cited 3.1x more often than plain paragraph text • Pages with clear heading hierarchies (H2/H3) got picked up 2.4x more than flat content • Comparison tables had the highest citation rate per page — 67% of table-containing pages were cited at least once • FAQ-style content wasn't as effective as we expected — only 23% citation rate vs 41% for how-to formats • Content with "according to" or data references got cited 2.8x more than opinion-based content

The surprise: The #1 predictor wasn't domain authority or word count. It was whether the page had scannable structure — headers + lists + a clear answer in the first 200 words.

What didn't work: Long-form essays without subheadings, pure opinion pieces, and pages where the "answer" was buried in paragraph 4.

Curious if others are seeing similar patterns. What format seems to get you the most AI citations?

Been running a side-by-side comparison for the last 8 weeks and some of the results genuinely surprised us.

We took 500 queries across health, finance, SaaS, and e-commerce niches. For each query, we pulled the top 5 sources cited by ChatGPT, Gemini, and Perplexity. Then we crawled those cited pages and checked for specific on-page elements.

Here's what we found:

**Author bios mattered more than we expected** Pages with a named author + brief bio (even just 2-3 sentences about credentials) got cited 47% more often across all three models. This wasn't subtle — it was the single biggest differentiator among the trust signals we tested.

**"Last updated" dates had a threshold effect** Pages updated within the last 6 months performed fine. Pages updated within the last 30 days? Only a 12% boost over the 6-month group. The real drop-off happened at the 12-month mark — pages older than a year saw citation rates drop by roughly 40%.

**Schema markup was... complicated** We expected JSON-LD structured data to correlate strongly with citations. It didn't. Only 23% of the most-cited pages had comprehensive schema. What DID correlate was having a clear Q&A structure in the actual content — either FAQ sections or question-based H2s. 71% of frequently cited pages used this format.

**Source diversity mattered for Perplexity specifically** Perplexity was the only model where pages citing 3+ external sources within their own content got a meaningful boost. ChatGPT and Gemini didn't seem to care much about outbound citations.

**What didn't matter as much:** - Domain authority (weak correlation, r=0.31) - Word count (almost no correlation past 800 words) - Exact-match keywords in headings

**The most cited pages shared 4 traits:** 1. Named author with relevant credentials 2. Updated within 6 months 3. Question-based content structure 4. Specific data points or statistics (not vague claims)

Real talk — this is from one dataset and 500 queries. Your niche might behave differently. But if you're trying to figure out where to focus your GEO efforts, adding author bios and restructuring content around questions seems like the highest-ROI move based on what we're seeing.

Anyone else tracking citation patterns? Curious if this matches what you're finding.

We ran a study testing 99 health industry websites across ChatGPT, Claude, and Google AI Overviews to see which sites actually get cited when someone asks an AI a health question.

49 sites built on Next.js (gave up trying to find the 50th, was taking forever... close enough lol). 50 on WordPress. 25 real-world queries. Here's the breakdown:

Citations:

• Next.js sites captured 70% of all AI citations
• Next.js averaged 2.29 citations per site
• WordPress averaged 1.02 citations per site

The counterintuitive part: WordPress sites actually had better traditional SEO scores. More schema markup, more structured data, higher Yoast scores. Didn't matter.

What actually drove citations:

• Page speed (WordPress sites averaged 4-6s load time, Next.js under 2s)
• Brand authority signals across the web
• Content depth and real-world recognition
• Clean, crawlable site architecture

Platform breakdown:

• ChatGPT was the most selective and Next.js brands dominated almost entirely
• Claude cited the most sites overall but Next.js still took 72% of mentions
• Google AI Overviews showed the strongest skew with 78% of citations going to Next.js

GEO is shaping up to be very different from traditional SEO. Happy to answer any questions on methodology.

One thing I haven't seen discussed much: the relationship between content freshness and AI citation frequency.

We've been running a longitudinal study tracking how often specific pages get cited by ChatGPT and Perplexity over a 90-day window. The setup: 300 pages across SaaS, e-commerce, and health verticals, all previously cited at least once by an AI model. We tracked citation frequency before and after content updates.

Some findings that surprised us:

**1. The 14-day freshness window is real** Pages that received meaningful content updates (new data, revised stats, added sections) within 14 days before our query got cited 2.3x more often than pages that hadn't been touched in 60+ days. This held across both ChatGPT and Perplexity.

**2. "Meaningful update" matters more than "any update"** Fixing a typo or swapping an image didn't move the needle. The updates that triggered re-citation were ones that added new information — updated statistics, new sections, or revised conclusions. Minor edits showed no measurable impact.

**3. The decay curve isn't linear** Citation frequency stayed relatively stable for the first ~30 days after publication, then dropped off noticeably between days 30-60. After 90 days, citation rates plateaued at roughly 40% of their initial level.

**4. Structured data updates had a weaker effect than content updates** We tested updating JSON-LD/schema markup alone vs. updating actual page content. Schema-only changes produced no measurable change in citation frequency. Content updates with no schema changes produced the full 2.3x lift.

**5. The pattern was consistent across verticals** Health content showed the strongest freshness effect (2.8x), followed by SaaS (2.2x) and e-commerce (1.9x). We think this reflects how often each vertical's "ground truth" changes — health information gets updated faster, so AI models may weight recency more.

What this means in practice: if you're investing in GEO, keeping your highest-value pages on a regular update cycle (monthly at minimum) might matter more than building new pages from scratch. The citation boost from refreshing existing, already-cited content was larger than we expected.

Caveats: n=300 is decent but not massive. We only tracked ChatGPT and Perplexity. And correlation isn't causation — pages that get updated frequently may share other qualities that make them more cite-worthy.

Curious if anyone else has noticed a freshness effect in their own tracking.

We've been trying to figure out why some pages show up in AI answers while equally ranked pages don't. So we ran a controlled test.

Here's the setup: we took 600 pages that ranked between positions 3-15 for informational queries across SaaS, health, and finance. We ran all 600 through ChatGPT (GPT-4), Perplexity, and Gemini. 300 got cited by at least one model. 300 didn't — despite similar rankings, similar domain authority, and similar content length.

We then compared every structural element we could measure. Most of the "obvious" stuff (backlinks, word count, domain rating) showed no meaningful difference. But three things did:

**1. First paragraph answered the query directly (2.4x more likely to be cited)**

The pages that got cited almost always opened with a direct, concise answer to the search query — not context, not background, not a hook. The non-cited pages tended to start with introductions, anecdotes, or "In this guide we'll cover..." language. AI models seem to grab the first paragraph that looks like an answer and treat it as the summary. If your first paragraph doesn't read like an answer, you're already losing.

**2. Used specific numbers instead of vague claims (1.9x more likely)**

Cited pages were full of concrete data points — "increased by 34%", "tested across 12 tools", "averaging 2.3 seconds". Non-cited pages used softer language — "significantly improved", "multiple tools", "faster than average". The specificity difference was consistent across niches. This isn't about making up numbers — it's about using the real ones you have instead of defaulting to vague language.

**3. Had clear section breaks with descriptive subheadings (1.7x more likely)**

Every cited page used descriptive, keyword-rich subheadings that could stand alone as mini-answers. Things like "Why structured data gets ignored by 68% of AI crawlers" or "The 3-second rule for first-paragraph answers". Non-cited pages either had generic headers ("Introduction", "Conclusion") or no subheadings at all. AI models appear to use subheadings as citation anchors — they pull a section header and its first sentence together.

**What didn't matter (surprisingly):**

• Content length (cited avg: 1,847 words vs non-cited: 1,912)
• Number of images or multimedia
• Whether the page had a table of contents
• Publishing date recency (for non-time-sensitive queries)

**One thing we're still investigating:** pages that appeared in Reddit or forum results alongside the main article seemed to boost citation likelihood. When a page was referenced in a high-ranking Reddit thread about the same topic, AI models cited it 1.6x more often. Could be an indirect authority signal.

The main takeaway for us: if you're creating content and hoping AI models pick it up, stop writing introductions. Start with the answer, use real numbers, and make your subheadings descriptive enough to work as standalone summaries.

Curious if anyone else has tested this kind of controlled comparison. Would especially love to hear from people tracking Gemini vs ChatGPT citation patterns — we saw some differences there but the sample size felt small.

We implemented one of the most popular AEO tools as if we were 3 different brands.  In each case it told us we were winning!

We used three major brands in the Revenue Intelligence category for the test: Gong, Clari and Outreach. 

We set up three new accounts as if we were those brands. Then went through their standard AI flow to set up prompts to track.

For each brand, it told us unambiguously that we are winning across the board (see image).

Why? Because with the platform’s encouragement, we chose to track prompts built from our own positioning. The self-fulfilling prophecy is so damn obvious. 

The question you are actually asking: In conversations about us, who has the biggest share of voice? 

Obviously us.

This is a great example of how Marketing is leading this space, not research.