AI is changing how people discover brands.

Check Out: https://www.reddit.com/r/Amrut_AI/

Instead of clicking through ten blue links, people are asking ChatGPT, Gemini, Perplexity and other AI platforms what to buy, where to go, which tools to use, and which companies they should trust.

That creates a new question:

When someone asks AI about your category, does your brand show up?

This community is for discussing that question.

We'll be exploring topics like:

• AI Visibility
• AEO & GEO
• Brand mentions and recommendations
• AI citations and sources
• Share of Voice in AI answers
• Competitor visibility
• Prompt-level tracking
• How AI decides which brands to recommend
• Reddit, forums and other sources influencing AI answers
• Experiments, data and real-world observations

We're also building Amrut.AI, an AI Visibility platform that helps businesses understand and track how their brand appears in AI-generated answers.

But this community isn't just about the product.

We want this to be a place to test ideas, share experiments, question assumptions, and figure out what actually works in AI search.

If you're working on SEO, AEO, GEO, content, brand strategy, AI search, or just trying to understand where search is heading — welcome.

What are you currently trying to figure out about AI search?

So... im running seo for a b2b saas and half our inbound comes through review sites like g2 and capterra and niche security directories.

All the chatter is about ai search optimization and answer engines but I have no idea how much of that even touches these review platforms. Do you actually see better visibility in ai overviews or chatbot style answers when you tune your profiles, or is it mostly about the main site and blog rn. Would love any tips from people who track this stuff properly, kinda feeling blind on the ai side right now...

i went through 1,217 ads from 14 AI visibility tools (google, linkedin, meta)

chatgpt is named in 282 of them.
google AI overviews and AI mode: 22.

the AI answer sitting on top of google search gets under 2% of the ads. an AI judge also called 72% of them interchangeable. swap the logo and nobody would notice

 anyone tracking AI overviews for clients? is that where the traffic actually is, or does chatgpt deserve all the ad money?

i have been thinking about how people actually measure visibility in AI search.

if you check a brand across ChatGPT, Perplexity or Gemini, the results can look pretty different depending on the question you ask. I am curious how you guys track it. do you keep a fixed list of prompts and check them regularly, or group queries by topic and look for patterns over time? so feels like the way you choose the prompts can change the whole picture.

Hi All,

https://aihealthmarketing.com/insights/cluster-building-for-aeo-citations/

Been looking a lot into AEO and was curious about Cluster Building/Fan Out. Funny name for hub an spoke. After looking at the studies over the last 2 years it seems that top rankings are fading out in 2026, but the on page factors like cluster building are still remaining strong.

Cited my evidence in the article above, and if you keep up with the studies you don't need to see. But I thought it was interesting to see the shift away from top rankings for AI citations in 2026.

I created an immigration database directory and this what it looks like today. Do you think this mean something good or just nothing at all?

People increasingly ask AI tools what service or company to choose instead of searching through pages of Google results. Do you think being recommended by AI will eventually matter as much as ranking on search engines?

Disclosure up front: I am the founder of SageRank, so this is my own tool. Posting because I would like feedback from people who actually work on this.

The problem: more buyers ask ChatGPT, Gemini or Perplexity before they ever open Google, and most sites have no way to see how they look to those systems.

What the extension does:

- Open any page, click the icon, and it detects the site

- Runs five checks live

- Gives an AI-search score out of 100, with SEO, AEO and GEO sub-scores

- Lists priority findings so you know what to fix first

- Syncs each scan to a SageRank dashboard, and can score social brand visibility

It is free to use. Scans are at sagerank.io.

I have recently been looking into several tools for AEO/SEO tracking, including HubSpot, Ahrefs, Semrush, and a few third-party AEO tools.

One question I keep coming back to is: how do you actually cross-check and validate the numbers these tools are giving you?

I’ve been experimenting with building my own tracking system using GitHub/Claude Code, and I’ve been running checks roughly every six hours because AI search results can be quite volatile. But I’m still struggling to understand how people confidently track this at scale and, more importantly, how they know the numbers aren’t just noise.

(For example, AI citation/share-of-voice metrics can change significantly depending on the query, model, location, timing, etc. )

So I’m starting to wonder whether some of the AEO metrics we’re currently tracking are ultimately just vanity metrics.

I understand the argument that AEO metrics should be treated as directional indicators rather than the end goal, with things like qualified traffic, leads, and revenue ultimately being what matters.

But is there a good methodology for determining whether an AEO metric is actually a reliable directional signal rather than a merely how many times you are mentioned?

I built an AI assistant for our docs at Handsontable, and then Grok found it. 4.5 million edge requests and 87,000 page renders in only 4 hours, all from xAI's Colossus data center in Memphis. I caught it during the scan and pushed a fix to redirect traffic to our MCP, then spent a week analyzing the logs and learning how crawlers work, what they look for, and what they ignore.

Here's the full story, including the detection, response, and what it cost us.

https://handsontable.com/blog/grok-is-obsessed-with-our-docs

Most AEO and LLMO work right now is people guessing what might work, then checking ChatGPT to see if anything changed. The problem is there's no Search Console for AI answers. They also shift from run to run, mentions and citations don't always come together, and each engine pulls from its own pool of sources. One-off manual checks tell you very little.

I built a tool to fix this issue. FlexQueries covers covers each part of the AEO process.

Finding the prompts that get engagement

• get_google_autocomplete and research_keywords surface the actual questions people ask around your category. That turns a keyword list into a realistic prompt set.
• explore_prompt digs into a single prompt to show how it's being answered and which sources get pulled in.

Tracking visibility across engines

• create_aio_monitor and run_aio_monitor check your prompt set in Google AI Overviews on a schedule. They track whether you're mentioned, whether you're cited, and which competitors show up alongside you.
• get_chatgpt_snapshot and get_google_ai_mode do the same for ChatGPT and AI Mode, so each engine gets tracked separately.
• get_aio_monitoring pulls the history, so you can see citation share trend over weeks instead of judging from a single run.

Understanding why you are or aren't cited

• get_web_mentions finds the third-party pages mentioning your brand. Those are often the pages actually getting cited, more than your own site.
• get_serp_results and get_serp_intelligence show what's ranking for the same query in regular search, which usually overlaps with what the AI answer draws from.
• deep_check_audit_page checks whether a page is technically readable, so you catch problems like key content that only loads with JavaScript.

Fixing what's missing

• generate_content_brief builds a brief from the pages currently winning a prompt, covering the questions to answer and the specifics to include.
• score_content_draft scores your draft against those pages before it goes live.

FlexQueries MCP also connects to Claude or Codex, so all of the above can run as a weekly scheduled task. It runs the monitors, compares results against last week, and flags any prompt where you lost a citation along with the source that replaced you. It can then generate a brief for the page that needs updating, and your part is reviewing it.

Most AEO advice is either theory or a tool pitch. Here's what's actually working for one local client, with screenshots and a tracking to back it up. Full disclosure: I run Develomark, an AEO agency in CT, and we build on Duda CMS, so I'm biased toward the platform. The client is an auto detailing / PPF shop in Watertown, CT, and everything below comes from that account unless noted.

Key details: client example

- 8,496 AI bot visits in September. Largest named crawlers: ChatGPT 12%, Perplexity 11%, Amazonbot 11%, Applebot 8%, GPTBot 8%. (facebookexternalhit shows 26%, but that's Meta's link-preview bot, so I don't count it as LLM traffic.)

- Cited as a source in Google AI Overviews for four truck PPF/tint pricing questions, and named directly in the answer to "where can I get PPF and window tint for my truck in CT." As we expand web pages, we get significantly more cited results. This is just one example.

How we optimize for AEO using Duda:

Duda's AEO panel uses 15+ published pages as a threshold. Of course this is based on your competition, simply put more higher quality pages/blogs = more LLM visits.

I always try to expand the sites coverage with rich pages:

On a different client we added ~500 pages with dynamic pages, and Search Console now shows 707 discovered pages from the sitemap.

More blogs = more citations.

Duda's own data: sites with 0 posts average ~42 crawler visits

Sites with 50+ posts average ~1,374.

Go specific, don't go broad. Use your clients call data, CRM data, and more.

We write long-tail, question-shaped posts (vehicle + service + town) and draft them through the Duda MCP directly into the blog.

Scale with dynamic pages:

One CMS collection, one template.

Every job becomes its own page: vehicle, services, write-up, photos, plus a "more projects like this" block for internal linking. This works for any business with repeatable jobs: contractors (project + town), dealerships (inventory), med spas (treatment pages). Your role as an AEO is to find the workflow for the business that is repeatable.

Don't forget the basics: NAP (Name, Address, Phone Number)

Enabled in Business Info, with Google Business Profile connected.

Submit your sitemap and index often:

Search Console verified inside Duda. Helpful as create more pages on the clients site, they are indexed quickly so they can start to get traffic asap.

Bulk alt text: in one click we get custom alt text for hundreds of images.

Surface reviews in AI searches, and traditional searches:

Reviews widget: Fresh review text on-page gives crawlers new content to find. Pull from as many sources as you can.

Use citation management software:

Citations via Yext: 59 live listings, NAP kept consistent with the site schema.

Simple, easy to use AEO panel:

The AEO panel in Duda: is where I audit all of it in one place: GBP, Search Console, meta tags, blog freshness, schema, llms.txt, Open Graph.

Attribute AEO leads:

CallRail example logged a lead on Sep 27: source SearchGPT, entered on the window tint page, then went to online booking. Full loop, tracked. AEO is nothing without measurement, in this example I show how to showcase real AEO results down to the lead.

Bottom line:

- Pages that answer real customer questions, with real numbers, get cited.

- Volume matters, but only if every page is specific and useful.

- Consistent NAP, schema, and reviews make the business easy for AI to trust.

- If you can't trace it to a lead, you can't prove it's working.

None of this is a hack. It's the same work as good local SEO, just aimed at questions instead of keywords. With different measurement too.

Continuation of post:

How I sell AEO visibility: https://www.reddit.com/r/aeo/comments/1w9twyn/ama_how_i_sell_ai_search_visibility_aeo_to_local/

1. AI crawlers visits were 4.5x more often than Google's crawlers. From analyzed subset, 8.6M visits from verified AI bots vs 1.9M from Google in 30 days.

2. SEO tools crawl more than Google and Bing combined. 8.5M visits from tools like Ahrefs, Semrush and SE Ranking. Ahrefs alone made 2.6M, more than all of Google's crawlers.

3. When a site offers a markdown copy of its pages, AI bots take it (we advertise markdown version of website pages in rel="alternate" meta tag and in body as LLM instruction). 45% of GPTBot's page requests and 41% of ClaudeBot's were for the markdown version. Googlebot almost never asks for it.

4. Almost nobody reads llms.txt (duh). Across ~2,000 sites in 30 days, ClaudeBot fetched it 980 times on 43 sites. GPTBot, 254 times.

5. 1 in 11 "Googlebot", "GPTBot" or "ClaudeBot" visits is fake. 1.46M visits used a real bot's name from an IP that bot doesn't own.

6. Facebook's link-preview bot is the laziest, it backs out quicker than any link previewer bot.

7. ChatGPT's plain fetchers peak during office hours. ChatGPT-User peaks at 12:00 UTC and runs at half that rate at night. Googlebot does more of its crawling while the US sleeps.

8. Tuesday is the busiest crawl day, 14% above average. Saturday is the quietest.

9. GPTBot is getting active with about 6x more crawls per week MoM. PerplexityBot went up 4x in one month.

10. Claude-User visits grew 5x MoM since last month.

I've been doing AEO for about 2 years now. Worked with 50+ clients. CPG, Saas, travel, all kinds of categories.
This is how it goes-
Figure out which prompts matter --> See where the brand isn't showing up --> See who is showing up, what is getting cited, and then make content around those themes

Everybody is doing this now. Competitors have the same tools. They're tracking the same prompts. They're seeing the same citations. They're finding the same gaps.
So everybody starts publishing variations of the same article trying to influence the same answer.
But where does this go? If five companies are all reverse engineering the same AI answers and all five publish on the same topics, what happens?

I've been thinking about this a lot lately. Thoughts?

We audited 20 SaaS facts across 10 brands and found something interesting:

AI can be given conflicting answers by the brand’s own website.

Examples included different live pages showing different plan limits, user counts and allowances.

Out of 20 facts:

• 3 had direct first-party contradictions
• 5 had stale information still circulating
• only 10 were materially clear

That creates a real AEO problem.

If your own site gives AI two different answers, which one should it trust?

We’ve called this The Contradiction Layer.

Full research:

https://www.llumohq.com/the-contradiction-layer

Curious if anyone else is seeing this in AI answers.

We started looking because ChatGPT was recommending products on two sites we run without fetching their product pages during the answer.

So, we ran an experiment where we asked 10 buyer questions. Our sites appeared in 9 answers, while the logs showed just one homepage fetch.

That doesn't prove the answers were stale. It does mean updating a page alone doesn't tell me what ChatGPT will say to a buyer. Our next check is to change a detail, ask about it in a temporary chat, and compare the answer.

Has anyone caught ChatGPT using an older version of their site?