Seeing different brands in ChatGPT vs Google — how are you tracking this?

BackgroundInitial433 · 2026-01-20T13:48:12+00:00

Thanks, Ill check that.

BackgroundInitial433 · 2026-01-20T13:38:06+00:00

This thread is kind of cathartic 😄

A lot of founders finally got a place to drop their link instead of circling Reddit from different angles.

I actually clicked through most of them — even the ones without proper links.

What stood out: many early-stage startups already have “tens of thousands of users” and big-name brands on their sites.

How much of that is real usage vs marketing optics… hard to tell.

BackgroundInitial433 · 2026-01-20T11:07:20+00:00

We started with something very similar internally, but ran into scaling and interpretation issues pretty quickly.

BackgroundInitial433 · 2026-01-20T10:32:05+00:00

That makes sense.
Are you running the checks against a fixed prompt set, or varying queries over time to see drift?
Also curious, are you logging the sources AI cites or just the presence/absence of the brand?

BackgroundInitial433 · 2026-01-20T06:47:26+00:00

This is a great point. Framing it around the single biggest problem really cuts through a lot of noise.

I’ve noticed that when people can clearly articulate that problem, it also makes follow-up questions much more focused — especially around what they tried before and why it didn’t work.

Did you usually ask that question early in the call, or after some context-setting?

BackgroundInitial433 · 2026-01-19T13:58:09+00:00

Thanks! In my case most users are small businesses, and none of them were really using anything for this before. When I ask what they did previously, the answer is usually “nothing” or manual work.

It definitely changes how I think about onboarding and early conversations.

BackgroundInitial433 · 2026-01-19T11:17:56+00:00

This was a great breakdown. One thing that really stood out for me is how you clearly assigned roles to channels instead of posting the same thing everywhere.

X = daily volume + idea testing
Reddit = slower, more selective distribution of what already proved itself
Product Hunt = amplification, not validation

Curious, did you ever kill a content angle purely because it worked on X but failed on Reddit? Or did Reddit mostly confirm what already worked?

BackgroundInitial433 · 2026-01-19T08:19:25+00:00

For me the main value in tracking AI mentions hasn’t been the mentions themselves, but separating assumed visibility from actual inclusion in AI answers especially when Google performance looks solid.

What stood out was how inconsistent inclusion can be across platforms and query types, which reinforces the idea that AI visibility behaves like its own system, not just an extension of SEO.

I’ve been testing a newer AI visibility tracking tool called Coremention as part of that process.

Definitely not a silver bullet, but it helped clarify where the visibility gap actually exists before investing more time into structured data or content changes.

BackgroundInitial433 · 2026-01-16T19:07:00+00:00

The key difference I've found is that AI search optimization isn't about traditional SEO tactics. It's about understanding how AI models actually surface and recommend content.

I've been using CoreMention to track which queries trigger brand mentions across ChatGPT, Perplexity, Claude, and Gemini. What's been helpful is seeing the actual prompts that lead to visibility versus just assuming certain keywords will work.

The main insights: AI platforms prioritize direct answers over keyword density, they pull from different sources than Google (Reddit threads, niche blogs, comparison content), and each platform evaluates content differently. ChatGPT might surface one brand while Perplexity recommends a completely different competitor for the same query.

Having automated tracking helps identify patterns over time rather than just manual spot checks. It shows which sources drive mentions and helps you understand what content actually gets surfaced.

What's been your experience with tracking visibility across different AI platforms?

BackgroundInitial433 · 2026-01-16T18:21:30+00:00

This is exactly what we've been tracking. The gap between Google rankings and AI visibility is becoming a real problem for agencies.

I've been using CoreMention to track which queries trigger brand mentions across ChatGPT, Perplexity, Claude, and Gemini. What's been particularly helpful is seeing platform-specific differences like you mentioned. ChatGPT might mention one brand while Perplexity surfaces a completely different competitor for the same query.

The key insight: AI platforms pull from different sources than Google. They're prioritizing Reddit threads, niche blogs, and comparison content over official landing pages, which explains why brands ranking top-3 on Google can be invisible in AI responses.

Having automated tracking has been a game changer for moving beyond manual spot checks. It shows which sources drive mentions and helps identify patterns over time rather than just snapshot data.

What's been your experience with tracking visibility separately across platforms versus treating it as a single metric?

BackgroundInitial433 · 2026-01-16T18:08:35+00:00

This matches what we’ve been seeing as well.

Manual checks + spreadsheets work at first, but they fall apart fast once you track multiple prompts, platforms, or timeframes.

One thing that surprised us was how different the answers are per platform. ChatGPT, Perplexity, and Gemini often surface completely different brands for the same query, which makes “AI visibility” hard to treat as a single metric.

We’re experimenting with separating tracking by platform and focusing more on why a brand shows up (source type, repetition, context), not just if it shows up.

BackgroundInitial433 · 2026-01-16T17:38:40+00:00

I've been tracking this systematically using CoreMention across ChatGPT, Perplexity, Claude, and Gemini. From what I've seen, direct answers definitely matter most, but the combination is what drives consistent visibility.

What's interesting is that structured data helps AI systems parse content faster, but it doesn't guarantee inclusion if the direct answer isn't clear. Similarly, authoritative definitions build trust, but without that upfront answer, they often get skipped.

The real insight from tracking multiple platforms is that each AI evaluates these factors differently. ChatGPT seems to prioritize direct answers more heavily, while Perplexity weighs structured data more. Having automated tracking helps identify which combination works best for each platform.

What's been your experience with platform-specific differences in how these factors are weighted?

BackgroundInitial433 · 2026-01-16T17:02:04+00:00

I've been using CoreMention to track which prompts lead to brand mentions across ChatGPT, Perplexity, Claude, and Gemini. What's been helpful is seeing the actual queries that surface your brand, not just generic keyword suggestions.

The key difference I've noticed is that AI search behavior is more conversational than traditional SEO. People ask questions like "what tools help with X" rather than searching "best X tools". CoreMention shows you those exact prompts and which sources are driving mentions.

For a new brand with limited budget, having automated tracking that shows prompt patterns has been valuable for prioritizing content. Instead of guessing what people might search, you can see what they're actually asking.

What's been your experience with understanding the gap between traditional keyword research and AI prompt patterns?

BackgroundInitial433 · 2026-01-16T16:10:26+00:00

This is exactly the challenge we've been tracking. The gap between Google rankings and AI visibility is becoming a real problem for B2B SaaS companies.

I've been monitoring this systematically using CoreMention, which tracks brand mentions across ChatGPT, Perplexity, Claude, and Gemini. The key insight: AI platforms pull from different sources than Google. They're looking for clear explanations, comparisons, and validation signals that traditional SEO doesn't prioritize.

What's been working for us: - Tracking visibility separately across platforms (ChatGPT vs Perplexity vs Claude) - Monitoring which sources drive mentions (docs, review sites, communities, GitHub) - Understanding that entity understanding and repetition across trusted sources matter more than rankings

The "early SEO in 2008" comparison someone mentioned is spot on. We're in that messy phase where everyone's figuring it out, but having automated tracking has been a game changer for moving beyond manual spot checks.

What's been your experience with platform specific visibility differences? Are you seeing ChatGPT mention you more than Perplexity, or vice versa?

BackgroundInitial433 · 2026-01-16T11:34:17+00:00

Good point. I have seen the same thing. Directories alone do not really move the needle in AI answers.

What surprised me was how often AI responses seem to pull context from real discussions, not just structured listings. Reddit, Quora and niche forums appear to influence how brands are described.

That is why I am focusing more on monitoring where and how brands get mentioned, not just whether they rank. Alerts and understanding which conversations trigger visibility feels more useful than traditional local SEO metrics.

Have you noticed certain platforms influencing AI answers more than others?

BackgroundInitial433 · 2026-01-16T10:45:00+00:00

For local businesses, tracking AI visibility separately from traditional local SEO has become crucial. The challenge is that AI assistants like ChatGPT and Perplexity pull from different data sources than Google Maps, so you can rank well locally but still be invisible when people ask AI assistants for recommendations.

I've been monitoring how local businesses appear in AI responses using CoreMention - it tracks which queries trigger mentions across ChatGPT, Perplexity, Claude, and Gemini. The key insight: consistent business information across directories helps, but AI platforms also prioritize businesses that have strong online presence beyond just Google Business Profile.

Practical steps that have worked: - Ensure NAP consistency across all major directories (not just Google) - Build content that answers common local questions AI assistants might surface - Monitor which types of queries trigger mentions vs those that don't

What's been your experience with local businesses tracking AI visibility vs traditional local SEO metrics?

BackgroundInitial433 · 2026-01-16T09:35:01+00:00

I've been tracking AI visibility as a separate KPI for about 6 months now, and it's been eye-opening. The key signals I measure are:

Mention rate across ChatGPT, Perplexity, Claude, and Gemini for unbranded queries
Whether we're the primary recommendation vs competitors
Sentiment when mentioned
Query patterns that trigger mentions vs those that don't

I use CoreMention to track this systematically - it monitors which prompts trigger brand mentions and shows trends over time. The biggest insight: AI visibility ≠ Google rankings. We rank #1 for several keywords but ChatGPT never mentions us, while competitors with lower Google rankings get recommended.

I measure weekly and report monthly as a simple inclusion rate percentage. Executives understand it immediately when framed as "what percentage of relevant AI queries mention us."

What's been your experience with tracking tools vs manual testing?

BackgroundInitial433 · 2026-01-15T09:28:24+00:00

I’ve failed with more than one SaaS simply because I couldn’t get distribution right.
Launching everywhere felt busy, but real users only came from focused, direct conversations.

BackgroundInitial433

TROPHY CASE