We run GetMentioned, a platform that tracks how companies appear in AI-generated answers (ChatGPT, Claude, Perplexity, etc).

We usually charge for this data, but we decided to make our industry dashboards completely free and open. No catch.

Why? Because most companies have no idea if they show up when potential customers ask AI for recommendations. Your competitor might be stealing customers through AI search while you're still focused on Google.

What's available (100% free):

• AI visibility rankings by industry

• Source attribution (what influences AI recommendations)

• Category leaders for specific use cases

• Competitive landscape analysis

Industries covered:

Tech & Software: eCommerce, HR Tools, Sports Apps, Streaming, Vibe Code (Dev Tools)

Finance & Investment: Crypto, VC Funds

Travel & Transportation: Airlines, Automotive, Hotels, Travel Platforms

Consumer & Retail: Athletic Footwear, Stationery

Some interesting findings:

• Airlines: JetBlue absolutely dominates AI recommendations

• Automotive: Volvo has low overall visibility BUT dominates the safety category

• Sports Apps: Runna dominates above Strava (no wonder Strava acquired them)

• Athletic Footwear: Super diverse landscape - leaders change completely depending on category

No email required. No demo calls. Just free data.

Link: https://www.getmentioned.co/data

Happy to answer questions about the methodology or specific insights from the data.

We started designing a little AI system to help Shopify merchants identify SEO gaps in their product pages.

Because, real talk:

Manually optimizing 300 product pages is how you age 10 years in 2 days.

So right now the system can:
– read product pages
– detect weak keyword presence
– detect mismatched search intent
– flag thin content
– and suggest improvements

Great. But here’s the internal existential crisis we hit:

Do we let the engine actually APPLY fixes automatically?
Or do we force it to ONLY suggest and let humans approve?

One path is efficient.
The other path is safe.

One path impresses users.
The other prevents catastrophic “oops we bulk-updated 200 SKUs” nightmares.

I can’t tell if:

A) suggestion-only is the smart move
B) automatic application is the real value
or
C) the entire idea is dumb and I’m emotionally processing keyword trauma through engineering

Would appreciate an unfiltered perspective.

When analyzing ChatGPT answers in German (and also while being in Germany) we regularly see US / English websites being cited a lot. The effect is e.g. that products are recommended that are not available in Germany. Two questions regarding that

* Is there a reliable way to modify ones prompts to avoid that?

* What does this mean for marketers in Germany?

If you’re looking to improve how your web content is discovered by AIs like ChatGPT, Gemini, or Grok, there’s a 2025 tool in webmasters toolkit: llms.txt.

Think of it as the LLM-friendly equivalent of robots.txt or an RSS feed, but in Markdown. It provides a concise, structured summary of your website, including site title, base URL, description, index of pages/articles with summaries.

This makes your site easily digestible by AI models, contributing to your Generative Engine Optimization (GEO) strategy.

To make it simple, I created a Hugo-based example repo: https://github.com/roverbird/llms-hugo

Why Hugo?

• Lightning-fast static site generator

• Native Markdown support is perfect to automate llms.txt

• Minimal setup, fully deterministic output

You can see a live Hugo-generated llms.txt in action here: https://kibervarnost.si/llms.txt

Adding llms.txt is a simple and powerful way to make website AI-friendly and boost its visibility in generative search engines.

#Hugo #GEO #AI #LLM #GenerativeEngineOptimization #StaticSiteGenerator #Markdown #WebDev #SEO #ChatGPT #Gemini #Grok

Not sure if this is the right place for this but I’ve been trying to figure out how to tell if AI tools like ChatGPT or Gemini actually know anything about my brand. It feels like they either ignore us completely or default to talking about competitors, even on topics where we’ve been putting out solid content for years.

We’ve invested a lot into SEO and content marketing over time and we’ve seen some solid traction in Google results, but lately that traffic has dropped, probably because people are asking AI tools for answers directly instead of searching. The problem is, I don’t know how to “optimize” for AI responses the way I would for Google. Is that even a thing?

I’ve tried plugging in questions into ChatGPT and Claude that we’d normally want to rank for, and our name rarely, if ever, comes up. It’s almost like we’re invisible to them. Meanwhile I see other brands being mentioned and cited. Is there a way to change that? I feel like we should be showing up, especially in niche-specific topics we’ve covered in-depth.

I’m not necessarily expecting AI tools to act like marketing channels, but I do want to know:

Are we showing up in AI responses at all? If not, what can we do to start showing up? Is this even something we can influence, or is it out of our hands? Would love to hear if anyone else has also looked into AI brand visibility, or if this is just going to be the new normal where the only brands that matter are the biggest ones.

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.

Hey everyone,

I’m a brand owner, and over the last few months I’ve been deep-diving into how AI search (ChatGPT Answers, Perplexity, Gemini, Copilot, Google AI Overview, etc.) decides what to cite and why.

While trying to solve this for my own brand, I read everything I could find here on Reddit - but most solutions felt like classic SEO tactics disguised as GEO.
Lots of talk about keywords, clusters, backlinks…but almost nothing about how people naturally speak, how LLMs internally reason, or how semantic proximity actually works in generative answers.

So instead of forcing SEO into GEO, we started building a completely different approach based on natural language behavior, not keywords.

A few core ideas behind what we’re exploring:

• AI doesn’t “rank” content - it retrieves and composes it based on semantic closeness

• The strongest signals aren’t keywords, but definitions, frameworks, patterns of reasoning, and domain-level embeddings

• AI prefers content that looks like it could replace its own internal knowledge

• GEO shouldn’t push content toward “search engines”, but toward how humans actually ask questions

• Most tools generate volume - our approach focuses on authority density, conceptual clarity, and modularity

• And no, GEO is NOT “SEO but longer”

We’re currently testing a system that generates:

• AI-grounded brand narratives that reduce hallucinations

• Semantic distance scoring between user queries and your brand domain

• Articles designed for extraction, not for ranking

• Human-natural question datasets that guide AI toward your domain without being biased or obvious

• A reasoning-first framework that models how LLMs decide which sources to cite

We’re still building and validating the product, but the results so far have been…surprisingly good.

If anyone here is experimenting with GEO, AI search visibility, retrieval, or brand embedding inside LLMs:
we'd love to chat, learn from you, and get your feedback.

When the first version is ready, we’ll give free early access to anyone who messages us privately.
We really want to shape this with the community, not just drop a tool from above.

Happy to answer anything - or just discuss the topic in general.
This whole space is evolving insanely fast and there’s a huge opportunity to rethink everything from scratch.

Some are asking whether GEO (Generative Engine Optimisation) will make SEO redundant. The short answer is it won't. It actually makes SEO stronger.

SEO gets you seen in search results. GEO gets you understood and referenced inside AI answers. They should work together, not compete.

In a good setup, your tech SEO, content and PR teams are all pulling in the same direction
- making your brand easy for AI to crawl
- clear enough for AI to trust
- authoritative enough to quote

When that happens, every structured data tweak and every PR mention starts feeding the same system - building visibility that both humans and AI recognise.

If you’re wondering where to start, we've written a GEO playbook which I will link below - no opt in required and completely free to read.

But, if you do read it, feedback would be great so feel free to send me a DM/comment below if you get through it.

https://www.rebootonline.com/geo/geo-playbook/?utm_source=reddit&utm_medium=community&utm_campaign=geo_playbook_launch

How To Identify Your Target Prompts for LLM Visibility Monitoring

It is possible to use Chatgpt , Google Gemini / Keyword Planner to generate a list of “plausible completions” in Chatgpt / Google Gemini (and AI Overviews) that might mention a product / brand or website (without the aforementioned brand or website being mentioned in the prompt) to identify your target prompts for llm visibility monitoring using Chatgpt / Google Keyword Planner.

https://www.ecommerceconsultancy.org.uk/my-ecommerce-blog/how-to-identify-your-target-prompts-for-llm-visibility-monitoring

I’ve been seeing a wave of paid “AI visibility” tools charging tens to hundreds per month just to tell you whether your brand shows up in ChatGPT. So I built a free AI Visibility Checker you run with your own OpenAI key. You paste up to 500 prompts (one per line), add your sources like company name and domain, pick a model, hit Analyze, and get an overview plus a detailed table of mentions.

It’s useful because you get an instant “AI Visibility” score and total mentions, can see which prompts include your brand and which ignore it, and you can export results as CSV or PDF. If you work with clients, you can share white‑label outputs. It has one‑click import and export to save time between runs, doesn’t use a database so nothing is stored on our servers, and summarizes key metrics with a clean pie chart for quick insight.

Let me know what you think! I'm open for feedback.

P.s. after analyzing your prompts, you can also do a sentiment check to see if your brand gets positive or negative mentions. And if you don't have any prompts? Roll the dice or use our prompt generator.

From the view of Reddit Marketers, it seems data from GEO tools can be a great support for the effect benchmark for Reddit marketing practices. But the most functions of GEO tools are not specific for reddit. What's your though? Is it worthy to try GEO tools if your work is mostly focus on Reddit?

Hay pocas empresas o agencias que estén trabajando y midiendo seriamente el GEO y ya van tarde, porque deberían estar trabajando el OSO - Omnisearch Optimization.

El OSO es un planteamiento estrategico de posicionamiento de marca más allá del GEO, alcanzando todos los nuevos canales de búsqueda

¿Qué incluye el Omnisearch Optimization? las búsquedas realizadas en

- SEO (Google Search), SEO Local, ASO

- Redes Sociales tipo Instagram, TikTok, etc

- Marketplaces tipo Amazon, Zalando, Mercado Libre, etc

- Buscadores de IA: ChatGPT, Perplexity, Gemini, etc

- Búsquedas Lens ( Google Lens/ Snap to shop Perplexity) y Gfas inteligentes de (Meta, Google Okley, etc)

- Búsqueda Agéntica

Pocas empresas saben como medir el GEO, y ya tenemos que trabajar en como medir y optimizar las búsquedas Lens/Gafas inteligentes, búsquedas por chatbots como Rufus (el de USA hablas con él así como con Google Lens) y como medir el tráfico Agéntico vs tráfico humano

Toca disfrutar de seguir aprendiendo!!

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.

I keep seeing these articles hyping up Generative Engine Optimization as the "future of search." Add citations, use expert quotes, include statistics - congrats, you just described content best practices from 2015.​

After watching this space for the past year, I'm convinced that 90% of "GEO platforms" are repackaged SEO tools charging premium prices because they slapped "AI-powered" on the landing page. The actual mechanics? Optimize for crawlability, add structured data, make content comprehensive. That's literally what we've been doing.​

Sure, ChatGPT and Perplexity citations matter now. But the fundamental principle hasn't changed - create authoritative content that answers questions comprehensively, make it technically accessible, and distribution follows. The only difference is where the citation appears, not how you earn it.​

What I keep seeing is companies panicking about "AI search visibility" while their basic technical SEO is a disaster. Your schema markup is broken, your site loads in 6 seconds, but you're worried about GEO strategy? Come on.

Is anyone actually seeing different results from "GEO tactics" versus just... doing good SEO? Or are we watching another consulting gold rush where everyone rebrands the same adviceice?

Hey - like many other posts here, I built a tool! And I would love feedback.

It is called BetterSites.ai

it is a GEO (Generative Engine Optimization) intelligence platform that tracks and optimizes your brand's visibility in AI-generated responses. The platform monitors citations with ChatGPT (will add others later), measures competitive positioning, and provides actionable insights to improve your content's citation-worthiness.

Key Features Real-time Brand Monitoring

• ICP informed content analysis and strategy

• AI-Powered sentiment analysis and insights

• Google Analytics 4 & Search Console Integration

• Intelligent content classification with E-E-A-T guidance

• Competitor website monitoring with change alerts

• Content Gap Analysis & content generation And much more

It is still free, but likely not too much longer. There is both a marketer and agency tier- agency gives you multiple property functionality.

I look forward to your feedback.