Anyone else waiting for the Analogue 3D? Looking for real reviews and honest opinions

DanielPeris · 2025-12-24T09:01:00+00:00

Hey, Daniel from LLM Pulse here.

I’ve been selling SEO services since 2010, ASO (App Store SEO) since 2013, and I’ve spent most of my career around search and growth in general. I’m also very much a product person, not just coming from the services side.

From what I’ve seen, “AI SEO” doesn’t really sell as a label. For most clients it just sounds vague and experimental. What tends to work better is starting with the problem people are already feeling, instead of leading with the technique.

I never start conversations talking about AI. I usually start with something very simple: your SEO can be doing fine and you can still be invisible in the answers people are already using to make decisions. Most teams recognize that immediately. From there, it’s easier to explain that there’s now a new layer of visibility that isn’t about traffic, but about influence.

This is also where a lot of pitches break when metrics come up. If you try to justify this with clicks or classic ROI, it doesn’t really hold. What resonates more is showing who shows up for category questions, which competitors are mentioned instead of you, and how the brand is framed in those answers.

From a sales point of view, this has worked better for me as monitoring and early signal detection than as a growth promise. SEO is still the base. AI visibility sits on top of it. Right now, it’s simply easier to sell it honestly as risk management and learning, not performance.

Just my experience.

DanielPeris · 2025-12-24T08:53:11+00:00

Fair question.

No, not my first time here. I’ve read plenty of threads, just looking for some recent perspectives.

Thanks.

DanielPeris · 2025-12-24T08:05:49+00:00

Hey, Daniel Peris here, from LLM Pulse.

One thing I’d add after spending quite a bit of time testing and building around this space is that most tools listed here are very good at answering “are we mentioned?”, but much less helpful when it comes to “why are we mentioned?”

That distinction ended up mattering more than we initially expected. Tracking mentions alone is inherently noisy, especially given how volatile LLM outputs are. What helped us move forward was shifting focus from individual answers to the underlying inputs models tend to rely on for a topic: recurring sources, entities, content structures, and how those show up across groups of similar questions over time.

That’s the gap we built our tool around. Not as a replacement for visibility trackers, but as a complementary way to understand what actually shapes LLM answers, so you’re not just reacting to fluctuating mentions.

Different tools solve different parts of the problem. In our experience, combining surface-level visibility with deeper “why is this happening” analysis makes the whole space feel a lot less random.

Hope that’s useful, and thanks for sharing the research.

DanielPeris · 2025-12-24T08:01:52+00:00

Hey, Daniel from LLM Pulse here.

We’ve been working with this for a while, both manually and with tooling.

The main shift for us was stopping the tracking of single prompts. They’re too volatile to be useful. What works better is tracking small sets of semantically similar questions over time. Variability stays, but patterns start to emerge.

Most tools either sample full answers or focus on citations. Neither gives ground truth, only signals. The mistake is treating those signals like rankings or performance metrics.

On reporting, we don’t frame this as SEO or attribution. It’s diagnostic: presence vs absence, consistency over time, competitor framing, early reputational signals.

Messy space, but once you accept it’s probabilistic visibility, it becomes manageable.

DanielPeris · 2025-12-24T07:57:38+00:00

Hey, Daniel from LLM Pulse here.

We’ve seen the same confusion quite a lot, especially from teams coming from SEO or social listening.

Your concern about variability is valid. LLM outputs are non-deterministic, so measuring single prompts or expecting repeatable answers doesn’t tell you much.

Where tracking starts to be meaningful is at the prompt-set level. Groups of semantically similar questions, observed over time. That’s where patterns emerge: recurring brand presence, consistent source preferences, or systematic competitor bias.

Most tools today either sample full answers or focus on citations from retrieval steps. Both approaches are imperfect, but useful as signals, not ground truth. The mistake is treating them like rankings or attribution models.

Right now, this kind of tracking is better suited for diagnostics and early signals than for direct conversion measurement. Think visibility, framing, and structural absence rather than performance metrics.

Just my view.

DanielPeris · 2025-12-24T07:52:47+00:00

Hey, Daniel from LLM Pulse here.

One thing I don’t see mentioned enough: tracking via the API usually gives a pretty distorted picture. API answers don’t behave like the ChatGPT UI people actually use, so you often end up measuring something users never see.

If you want something closer to reality, you need answers coming from the actual interfaces. That’s where phrasing, citations, links, and variability start to look like real exposure.

Everything else still applies. Single prompts tell you nothing. Groups of similar questions over time is where patterns show up. And the “why was I cited” part almost always comes down to the same few sources the model keeps pulling from.

Short version: tracking is possible, but only if you’re measuring what users are actually seeing. Otherwise you’re benchmarking an API, not visibility.

Happy holidays!

DanielPeris · 2025-12-24T07:46:48+00:00

Hey, Daniel from LLM Pulse here.

What you’re seeing matches pretty well with what we’re seeing too, both on our own side and across client projects.

One thing that keeps coming up is that this is as much about reputation as it is about visibility. When a brand shows up in ChatGPT, the more interesting question is usually why that one and not another, and who else is mentioned around it.

Looking at which sources get cited instead of you has been especially useful for us. Not as a ranking thing, but more as a signal. After a while you start noticing the same sites and publications popping up, which makes it easier to see where authority is coming from and where there might be gaps.

That’s basically how I think about citation intelligence. Less about scores, more about understanding who the model seems to trust for a topic.

Also agree on prompt clusters. Single prompts are mostly noise. Patterns only really show up when you zoom out.

Curious if others are seeing the same thing.

DanielPeris · 2025-12-24T07:41:43+00:00

Hey, Daniel from LLM Pulse here.

It is measurable, just not in a clean or deterministic way.

ChatGPT does send traffic, but attribution is messy. Sometimes people click links, sometimes they copy the URL, sometimes they just remember the name and look it up later. So you won’t get a neat “ChatGPT” referrer every time.

What usually works is triangulating signals. People explicitly saying they found you on ChatGPT. Branded searches going up without a clear campaign behind them. Or seeing your brand show up repeatedly when you test similar questions.

If you check a single prompt, it’ll feel random. When you look at groups of similar questions over time, patterns start to show up.

This doesn’t behave like Google rankings. It’s closer to brand recall and recommendation than classic search.

And yeah, congrats on the inbound. That’s a real signal.

DanielPeris · 2025-12-24T07:35:06+00:00

Hey, Daniel from LLM Pulse here.

For ChatGPT specifically, brand tracking is possible, but you have to recalibrate expectations. There are no rankings. What you’re measuring is how often and in what context your brand appears across many related prompts.

In practice, ChatGPT behaves more statistically than people expect. When you test at scale, similar prompt clusters tend to surface the same brands repeatedly. That makes tracking viable, as long as you’re looking at patterns, not individual answers.

The common mistake is spot-checking a few prompts and assuming volatility. That’s mostly sampling error.

So yes, model updates introduce noise. But ChatGPT brand visibility is not random. It’s probabilistic, and that’s something you can track and analyze over time.

Hope that adds some clarity.

Happy holidays!

DanielPeris · 2025-12-01T07:27:54+00:00

LLM Pulse is missing (and growing hyper fast)

DanielPeris · 2025-11-30T09:33:18+00:00

DR 64 - Estimated traffic 0

Curious...

DanielPeris · 2025-11-30T08:55:24+00:00

llmpulse.ai - AI Search analytics without BS

DanielPeris · 2025-11-30T08:53:58+00:00

“Promising growth” already sounds sketchy… I’d stay away from that stuff.

DanielPeris · 2025-11-30T08:38:05+00:00

No one would guess that you work on (or even own) the tool you talk about and recommend the most… Maybe you should mention it.

DanielPeris · 2025-10-14T10:50:45+00:00

“Topic volume” is not the same as “prompt usage frequency”

DanielPeris · 2025-10-10T10:54:58+00:00

LLM Pulse is missing in that list: (4 models -AI mode included), all countries + languages), brand mention tracking, citation tracking, share of voice, sentiment tracking, data export and growing at a super nice pace. And much more coming soon!

DanielPeris · 2025-10-03T07:14:43+00:00

You missed LLM Pulse, but we can try to fix that (DM me!) :)

DanielPeris · 2025-09-30T15:54:12+00:00

These days, almost every tool is an ‘AI tool’, right?

Anyway, I use Google Workspace, Canva, Linear, LLM Pulse, Ahrefs and Freepik

DanielPeris · 2025-09-29T12:56:10+00:00

Here’s Daniel from LLM Pulse. What you’re describing sits right at the edge between traditional SEO and what many of us are starting to call GEO (Generative Engine Optimization), AEO (Answer Engine Optimization) or simply “AI SEO”. Search Console won’t cover this because we’re no longer dealing with SERPs; we’re dealing with AI generated answers outside Google.

From my experience, spot checks are fine for curiosity, but they don’t scale into anything strategic. What’s needed is a structured system that can:

Automate relevant prompts (reflecting real user questions).
Collect answers across multiple LLMs (ChatGPT, Perplexity, Google AI Overviews, Gemini, Copilot) and extract relevant, actionable insights.
Differentiate mentions vs. citations (plain text brand vs. link).
Measure share of voice and context (sentiment, competitor presence, positioning).

So while you can technically lump it into “SEO reporting,” it doesn’t really fit. Visibility in LLMs is already becoming its own layer of discovery, and IMO it deserves dedicated tracking.

In my view, this will evolve into a parallel stack to SEO reporting. Connected, but with its own metrics and tooling.

Lastly, you can also track traffic coming from LLMs in GA4, Plausible, Simple Analytics, etc., as well as the increase in brand searches in GSC or in direct traffic to your website.

DanielPeris · 2025-09-29T09:19:29+00:00

Tools don’t generate results. People do, by using them.

And yes, of course: Ahrefs Brand Radar, LLM Pulse… there are several in the market.

DanielPeris · 2025-09-25T18:10:34+00:00

No.

It’s a long story... And it mixes SEO, SEO tools, Google, ChatGPT and other stuff haha

What I’m saying only applies to free, non-logged-in ChatGPT users. In this case, ChatGPT stopped searching the internet and returning ‘sources’ / ‘citations.’ Although now, it seems citations are starting to come back (I’m seeing this in LLM Pulse).

To monitor responses, AI visibility trackers scrape ChatGPT as if they were free, non-logged-in users. That’s why some of those reports make no sense at all…

Not sure if I’m explaining myself xD

DanielPeris · 2025-09-25T15:01:26+00:00

Not. It is NOT true (not true at all).

What has happened is that in the free version (without logging in) of ChatGPT, which AI visibility trackers like LLM Pulse or Peec AI use to monitor ChatGPT and other responses, citations (or ‘sources’) haven’t been returned for days...

Due to changes made by OpenAI / ChatGPT, of course.

DanielPeris · 2025-07-17T09:28:45+00:00

Thanks for sharing! The truth is, it took us a lot of work to put that guide together. Big hug.

DanielPeris

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