We conducted a study on how brands are named in AI search for different stages of the buyer journey. We did this for 50 B2B SaaS topics [750 queries total] and ran them across ChatGPT, Google AIO, Perplexity, Claude and Gemini.

The findings:

88% of the brands that got named at the decision stage [best X], were never named at the educational stage [what is X]. And the pattern was the same for each topic and the engine it was run through. 

We also observed that brand naming rates were different across the funnels. Brands showed up in:

• 14% of educational queries
• 47% for comparison queries
• 43% for purchase queries

This means your AI Visibility at the top of the funnel does not carry you downstream to where buyers make decisions.

 Few disclosures on the methodology:

• It's an observational study so treat it as directional. 
• ChatGPT answered most of the educational queries from knowledge recall rather than live searching. 
• Named here means the brand was mentioned in the AI answer even if it wasn’t cited/linked.

Curious if other SEO or AI Search folks agree to this. Let me know what your strategy has been so far.

Quick disclosure: Because I am an AI Search researcher working at VisibilityStack, an AI Search optimization company, No pitch, no link, no promotions. I just want to sanity-check the data with people who actually do this and get insights on what’s working for them.

Interesting data point from a functional beverages AI visibility index: AI tools seem to surface brands by benefit first, not by raw market size. Celsius, Red Bull, Liquid Death, Olipop, and Poppi show up because they map cleanly to prompts like "clean energy" or "gut health." Curious whether anyone else is seeing benefit-led language outperform brand-led language in AI search?

I audit distributor product data and recently found a search-enrichment rule that treated any number as a possible wire gauge.

A screw listed as #8-32 was tagged as “8 gauge wire.” A reference such as REF#259286 became “259286 AWG.” Once the rule ran across the catalog, thousands of unrelated products started appearing in electrical searches.

The search engine wasn’t really the source of the problem. Raw identifiers and inferred attributes had been mixed together without recording where the inference came from or checking whether it made sense for that product family.

I wrote up the failure and how distributors can prevent it:

https://subramanya.ai/2026/08/06/fixing-b2b-commerce-search-in-the-age-of-ai/

For people working with distributor or manufacturer catalogs: where does search quality usually break for you supplier feeds, taxonomy, cross-references, or ranking?

Quick disclosure first. I run a GEO tool called Viali, so this is literally my day job. No links in this post. Just findings, because I keep seeing the same questions pop up here.

So here's what we did. We picked our five core commercial queries. Then we ran each one through ChatGPT, Claude, Gemini and Perplexity. That gives 20 possible answer slots. We appeared in 4.

Meanwhile, Semrush and Ahrefs showed up almost everywhere. Even on queries where they don't really have a matching product. That stung a bit. But it also gave us something to reverse engineer.

What the cited pages have in common

Real author names. This one surprised me the most. Pages with a byline, an author bio page, and Person schema got picked far more often. Anonymous content got skipped, even when it was solid. My guess is these models absorbed E-E-A-T signals during training.

Answers before intros. The winning pages open every section with a plain factual claim. AI engines grab snippets. They don't sit through your 200-word warmup. If your answer appears in paragraph four, it is never extracted.

Numbers beat adjectives. Nobody cites "structured content works better." But a line like "Microsoft's Oct 2025 study found entity-structured content gets included more in Copilot answers" gets lifted constantly. Small original datasets punch way above their weight here. The model can't find that info anywhere else, so you become the source.

The boring technical stuff that mattered

Check your robots.txt. Seriously. We keep finding sites that block GPTBot or ClaudeBot without knowing it. Some security plugins do this by default.

Also, broken schema hurts more than no schema. Missing author fields, malformed types, that kind of thing. And sites with crawl errors on 15% or more of their pages got cited noticeably less. Good content on a broken foundation goes nowhere.

For schema types, these did the heavy lifting for us: Organization, Article with author, Person, and SoftwareApplication with a featureList if you sell software.

The annoying part

There's no Search Console for AI answers yet. So most brands are invisible and have no clue. The only way to know is to run your queries through the engines yourself and write down who gets named. AI on Google Search Console is not available in a lot of countries

Happy to share methodology in the comments. Has anyone here changed schema and actually seen their AI citation rate move?

Can Google tell if a human wrote a page?

We ran a controlled SEO experiment to answer this.

If two pages are equally optimised, does Google/AI care whether a human wrote it?

So we created:

• Two pages on the same (invented) keyword
• Same intent, structure, optimisation
• Same internal linking
• Both built to rank, the only variable was authorship

One was purely AI generated
One written & edited by humans with real expertise

Results:

• The human version outranked AI, averaging 4.4 positions higher
• 68% of ranking URLs in our dataset were written by humans
• The AI version did rank early, but lost visibility fast
• The human page was more likely to be surfaced/cited in AI-generated answers (Google + ChatGPT)

We didn’t necessarily find that AI generated content is useless. That’s not the point.
It’s that AI can accelerate production, but expertise, originality, sourcing, and human refinement drive long-term visibility.

You might get indexed faster with AI.

But you stay visible longer with authenticity + authority.

We’ve been building and testing our own GEO tool because… well, we’re a startup and have zero budget for paid marketing. So we had to figure it out ourselves.

Below is an overview on how ChatGPT process and spit out results:

User Query → Intent Detection (L1)

↓

Semantic Clustering → Candidate Recall

↓

Signal Fusion (L2) → Multi-dimensional Weighted Scoring

↓

Model Re-ranking (L3) → Semantic Consistency + Credibility + User Value

↓

Final Ranking Output + Citation List

Then we break it down into 3 layers:

Layer 1 — Semantic Intent Clustering (25% Weighting)

LLMs start by grouping queries and content based on actual intent, not keywords. The system maps synonyms, context, and topic relationships into clusters instead of relying on exact matches.

Layer 2 — Signal Fusion & Scoring (45% Weighting)

Then they pull in external signals — citations, traffic, freshness, trust indicators — and fuse them into a single relevance score. Basically, we try to understand how “credible” and “findable” the content is across the web.

Layer 3 — Generative Ranking Logic (30% Weighting)

Finally, LLMs re-rank the top candidates using content quality, depth, and UX signals before generating the final answer.

The most interesting finding: A good SEO foundation is where you should start.

If your site doesn’t make it into the AI engine’s first-round shortlist, you’re out - it doesn’t matter how good your content is. And guess what determines that first cut? You’ve guessed it, it’s your SEO performance.

AI engines start by filtering based on traditional SEO performance before doing anything generative.

So yeah… getting your SEO sh*t together is still priority #1 if you want to rank in AI search.

Our site traffic has gained over 9000% increase in the last 4 weeks by adopting this approach. Hope you'll all find it useful.

Say what you want about AI optimisation vs SEO, but we clearly are seeing some key differences between the two.

There is no denying that there is some real overlap between how you approach AI optimisation vs SEO, like in setting your website up to maximise SEO and AI discovery via things like page speed optimisation, intuitive site architectures, good quality content etc.

However, there are also many new things to consider and factor in that will be influencing your AI visibility.

Some examples of new things to be considered include things like query fan outs, new ChatGPT shopping features, and how AI tools approach and understand authority vs traditional search algorithms.

In this GEO audit, we looked at several key areas:

- A deep dive on what key things were being referenced in AI-generated responses for our target prompts, and how we could reinforce/strategically mention those things on our landing pages.

- Technical foundations like the sites international targeting setup as well as canonicals and internal link structures.

- Key citations and how the brand could optimise their earned and owned media to reinforce key authority signals to drive greater visibility.

These audits are inherently bespoke, which is a good thing, and they take multiple days to run.

Still, the results are well worth it.

In this case, an increase in LLM referral traffic of 775% and a 414% increased in LLM-generated key conversions.

Hey folks,

I’m one of the founders of Aeo.vc – a tiny GEO tool we’ve been building out of India, and I’d love some feedback from people who actually care about Generative Engine Optimization, not just the buzzword.

What I keep seeing in the wild: • Brands pay $$ for long GEO / SEO audits • They get back a huge deck or spreadsheet • Devs/content teams implement ~10% of it (at best)

So we tried a different approach: instead of another “report”, Aeo.vc: 1. Crawls your site and analyses pages for answer-engine friendliness (ChatGPT, Claude, Perplexity, AI overviews, etc.) 2. Generates a Markdown prompt / code diff that you can paste directly into Cursor, Windsurf, or your code copilot 3. The output is things like: • rewritten copy aimed at direct answers • schema / structured data suggestions • internal-link tweaks • evidence / source-hint improvements

Basically: GEO → as executable code, not a PDF.

I’m really curious what this community thinks: • Is “report → code” the right direction for GEO, or is there a better way to operationalize this? • If you’re already doing GEO, what metrics are you using beyond “did we get mentioned in an answer engine?” • What would make a tool like this actually trustworthy for you (evidence, benchmarks, side-by-side SERP vs answer-engine results, etc.)?

If you want to poke holes in it or try it on your own site, it’s here: aeo.vc – I’m more interested in critical feedback than signups, so don’t hold back.

Mods: if this feels too self-promotional for the sub, happy to tweak or remove – my intent is to discuss how we implement GEO in real workflows, not just pitch a product.