u/search_to_sale

Been deep in the AI citation rabbit hole for the last several months and wanted to share what's actually working vs what's just noise. Curious if others are seeing the same.

1. Being cited by sources that LLMs already trust matters more than your own domain authority

This is the biggest mindset shift. Traditional SEO trained us to think about our backlinks. For AI visibility, what matters is whether you're mentioned on the sources the model treats as reference material — Reddit threads, industry roundups, Wikipedia-adjacent sites, niche news outlets, YouTube transcripts. Get mentioned there and you start showing up in answers even without ranking #1 on Google.

Practical version: stop obsessing over your own page and start tracking where competitors are getting name-dropped. Then go earn mentions in those same places.

2. Structured, declarative answers beat clever copywriting

LLMs lift sentences that are direct, self-contained, and factual. "X is Y because Z" structures get pulled. Marketing fluff doesn't. I've started rewriting key pages so the answer to the obvious question is in a single, quotable sentence near the top. Citations went up noticeably.

3. Perplexity and ChatGPT behave very differently

Perplexity leans heavily on fresh web results and cites generously — it's almost a search engine with a wrapper. ChatGPT (especially in non-search mode) leans on what's baked into training plus a smaller live retrieval set. Optimizing for one doesn't automatically get you the other. Worth tracking them separately.

4. The "FAQ + schema" tactic is overhyped

Lots of GEO advice is basically just rebranded 2018 SEO — slap an FAQ schema on it and pray. In my testing this does almost nothing for AI citations specifically. What works is having genuine, useful, quotable sentences in your content, schema or not.

5. Brand mentions without links are now valuable

This one's wild for anyone who came up in link-building era SEO. LLMs pick up on entity associations, not just hyperlinks. A Reddit comment that says "I've been using [your brand] for X" can contribute to citations even with no link at all. Unlinked mentions are a real signal now.

What's everyone else seeing? Specifically curious:

• Anyone tracking citation share over time with a specific tool? What are you using?
• Has anyone seen Google's AI Mode behave meaningfully differently from regular ChatGPT/Perplexity patterns?
• Is anyone NOT seeing the "Reddit dominates LLM citations" effect in their niche?

I've been looking at my client's competitors in Peec, and the newer URL report caught my attention. This is how it works:

For every cited URL (yours, competitor's, or third-party), Peec returns an analysis that includes:

• How often retrieved over time
• Prompts it is retrieved for
• AI chats that cite the URL and whether your brand is included in those answers.

This last thing was a little eye-opening as my client wasn't surfaced in any of those chats but the competitor was (and their URL was cited as noted). So basically, the competitor is found through it owned content!

This offers so much actionable insight into creating your editorial calendar:

• Analyze the competitor's successfully cited URLs
• See if those citations help the brand get included in the answer
• Create content that solves the same problems (but better)
• Wait for that content to rank (or help it) to increase its chances of getting cited
• Watch your brand included in the chats!

https://preview.redd.it/9wm16wdwrywg1.png?width=1344&format=png&auto=webp&s=b0dd1ab1790178976c69533d82fbe7a8cf62e625

No need to chase each opportunity, but make sure to analyze the top citations with a strong retrieval pattern, i.e., those that are cited by various models over and over again.

According to Kevin Indig, an analysis of 6.8 million subheadings reveals a measurable correlation between specific heading structures and ChatGPT citation rates.

• Question Formats Show Higher Alignment ChatGPT. Fanout queries are frequently phrased as questions. Consequently, question style headings appear to align more naturally within the embedding space, matching fanout queries at 1.5x the rate of declarative headings.
• The 20 to 39 Character Range Yields Peak Rates. Heading length shows a clear impact on performance. The 20 to 39 character range correlates with the highest citation rate at 32.7 percent.

https://preview.redd.it/f1g8z6xj5gwg1.jpg?width=1120&format=pjpg&auto=webp&s=dc617129b1e69e426b784282394adbbb6034b1fa

Source

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."

2. 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.

3. 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.

4. 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.

5. 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.

My team and I have tested a bunch of AI search tools during the last few months, we had different clients with different needs and were looking for a tool that adds the most value, affordable and reliable.

Here are our findings and I am not even going to tell you which one we have chosen so you can make up your own mind based purely on the pros and cons.

Important context: we hoped to find a tool that tracks mentions accurately, then we realized that this is impossible. There is no such thing as accurate mention tracking in AI search. LLMs are not deterministic duh

We then changed our criteria and started looking more at robustness, usefulness ability to connect with other apps and ease of use. Mention tracking is good for benchmarking over time and on scale, but not for making decisions based only on what the dashboard shows.

This also means every dashboard will give you different results. Do not be fooled by it and use this data with caution. In general I think the key is to combine a few data sources, really analyze them, and then make a decision based on experience.

1 - Peec AI

We tested it first. Their name was all over and it was kind of an obvious choice. Also what appealed to us was the tracking method. They scrape search data to identify how people search and then use it to test queries.

Peec AI is a solid tool. It is really intuitive and easy to use. Probably one of the easiest to get into.

Pros:

• very clean UX
• easy to onboard and start getting data quickly
• decent competitor view
• sentiment is there and easy to understand on a high level
• good if what you want is a straightforward visibility dashboard

Cons:

• in our opinion it is mostly a monitoring tool
• you get signals but not much help on what to actually do next
• no real owning of the outcome
• no meaningful traffic / conversion connection
• like with all these tools, the mention data itself should be taken carefully

Bottom line: good clean tool, probably one of the best if you want simple monitoring and do not want something too heavy.

2 - LightSite AI

This one is more holistic and the experience is different, not a dashboard but an agent you can communicate with

This is the only one we tested that actually felt like it is trying to own the outcome and not just show another dashboard.

It combines a few things that we think need to be combined if you actually want to make decisions:

• LLM mention tracking based on a mix of scraping and API style collection
• bot traffic analytics
• Sentiment analysis with NLP
• human visitor analytics from LLMs
• page level analytics
• technical data layer for the website - sort of structured data alyer
• an agent that actually sees the data, analyzes it and helps do something with it - it connects to GSC and Analytics data

This part was the most different. It did not feel like “here is your chart, good luck”. It felt more like “here is what is happening, here is what matters, here is what I can do for you next”.

You can connect more real business data into it, including traffic and search data, and then the system can actually identify opportunities, create content ideas, spot listicles, suggest outreach and in some cases even prepare the outreach.

That is a very different category of product in my opinion.

Pros:

• the most complete / holistic view we saw
• combines technical side and content side
• tracks both bots and humans, which is important
• much closer to actual outcomes and not only visibility
• agentic experience is very strong - it writes good content, find listicle oportunites and creates outreach campaigns and executes them (this was was very cool)
• feels like a system that analyzes your data rather than just storing it in charts
• best fit we saw for people who actually want help making decisions and moving

Cons:

• this is not a lightweight plug and play dashboard
• it requires website integration
• if you do not have a website or someone who can integrate it properly, this is probably not for you
• may be too much for people who only want a simple visibility tracker

Bottom line: if all you want is a dashboard, this is probably overkill. If you want something that actually tries to improve the outcome and something more holistic but without being charged an arm and a leg for

3 - Otterly

Otterly felt a bit more operational than Peec. Not in the sense that it does the work for you, but in the sense that it gives more substance around what might be wrong.

The GEO audit was probably the strongest part for us.

Pros:

• very solid audit
• good coverage across engines
• helpful for identifying technical and content gaps
• pricing felt reasonable for what you get
• setup was fairly easy

Cons:

• the UI is not bad but it feels more fragmented
• a lot of tables and views that are a bit disconnected
• still mostly observational
• no real owning of execution
• no real attribution to visits / pipeline / outcomes
• some things felt stronger in the docs than in the actual product

Bottom line: if your team already knows how to execute and you just want a pretty decent audit plus visibility tracking, this one is worth looking at.

4 - Profound

Profound felt more enterprise to us. More polished in some ways, but also more opinionated and less flexible.

It looked good. It felt premium. But for some of our clients it also felt like a lot of money for something that is still mostly around visibility and reporting.

Pros:

• polished product
• good sentiment analysis
• strong enterprise feel
• better than most at making the product feel serious and mature
• for large brands I can see the appeal

Cons:

• expensive
• less relevant in our opinion for smaller companies or scrappier teams
• not really built for people who want to move fast and do a lot themselves
• some of the more interesting attribution pieces seem more useful for bigger setups
• again, not really owning the outcome

Bottom line: if you are a bigger company and want a more premium enterprise style platform, it makes sense. For a lot of normal companies it felt too expensive for what it actually helps you do.

5 - Scrunch

Scrunch was interesting. Strong coverage, pretty configurable, and it felt like a serious visibility platform.

We liked that it covered a lot and that it gave more flexibility around prompts and setup.

Pros:

• broad platform coverage
• good configurability
• decent UI
• useful if you care a lot about monitoring across many engines and prompts
• more agency friendly than some others

Cons:

• still very much a monitoring first product
• not enough actionable guidance for us
• competitor analysis was fine but did not always explain why somebody else is winning
• you still need your own people and your own workflow to turn the data into action

Bottom line: strong monitoring tool, especially if breadth matters to you. But again, you need to bring your own brain, your own process and your own execution.

My overall take after testing all of this:

I think the market still confuses tracking with truth.

These tools are useful, but mention tracking alone is not enough and in some cases can be misleading if you take it too literally.

The best tools in this category are not the ones with the prettiest charts. They are the ones that either:

1. help you understand what to do next
2. help you actually do it

That is how I would use if I were choosing today.

Ahrefs is tracking traffic share using its own free analytics data, and these numbers overall align with what I am seeing as well:

Overall AI traffic: ~0.26% vs Search (down to 32%)

https://preview.redd.it/xi3uupqc2fug1.png?width=2200&format=png&auto=webp&s=4074c218b676ac22597a6bfeb5f0aa2a51a6828e

If you look at the data by channel:

• Google: 30.5%
• Bing: 1.36%
• DuckDuckGo: 0.27%
• ChatGPT: 0.21%
• Perplexity: 0.02%
• Gemini: 0.02%
• Claude: 0.01%

https://preview.redd.it/noftv4fu2fug1.png?width=2282&format=png&auto=webp&s=b5be526383b3a0a109b85e3b83cb5ee533d78d56

Source: https://chatgpt-vs-google.com/

So if you are still measuring LLM visibility by traffic, you are doing it wrong :)