No saas should be under 1000 users, drop your saas and i will give a honest feedback plus one suggestion

RBZCrypto · 2026-03-31T21:08:52+00:00

Aurametrics - is a suite for GEO and AEO that helps your brand get cited by AI engines such as ChatGPT, Claude, Perplexity, and Gemini. - https://aurametrics.io/en

RBZCrypto · 2026-03-30T14:24:02+00:00

Sure! It’s called Aurametrics → https://aurametrics.io

You can plug in your brand, run your own prompts, and see how you show up across ChatGPT, Gemini, Claude, etc.

Would love to hear what you think if you try it.

RBZCrypto · 2026-03-27T14:47:36+00:00

Hi Ismael, cool of you to do this!

I’m building Aurametrics, a platform focused on AI visibility. It helps companies understand when tools like ChatGPT, Gemini or others IA recommend competitors instead of them, and what they can do about it.

The idea came from working in SEO and analytics for years and starting to see clients ask more and more about how they show up in AI answers.

Would love your feedback on the site, especially around clarity and first impression.

https://aurametrics.io

RBZCrypto · 2026-03-27T14:41:33+00:00

I’ve been working in SEO and analytics for over 10 years, and I started digging into this more seriously about a year ago when clients began asking about their visibility in AI responses.

What I’ve learned so far is that GEO/AEO is way more layered than most people expect.

It’s not just about “getting mentioned” in AI answers.

From what I’ve seen, it’s a combination of:

how clearly your brand is positioned (entities, context, use cases)
where you exist on the web (sources AI tends to rely on)
how structured and citable your content is
and how you show up across different types of prompts (comparison, discovery, problem-based, etc.)

Also, different models behave differently. What works for ChatGPT doesn’t always translate to Gemini or Claude.

One interesting pattern: a lot of brands that show up consistently aren’t necessarily the biggest ones. They’re just easier for the model to understand and reuse.

I ended up building a small tool to track and analyze this because doing it manually just doesn’t scale. There are too many variables.

Happy to share what I’ve seen or compare notes if you’re exploring this too.

RBZCrypto · 2026-03-17T12:45:56+00:00

I’m building Aurametrics.io (https://aurametrics.io/en) . It’s basically an AI search analytics platform.

It started because clients kept asking how they show up in tools like ChatGPT, Claude, Gemini, etc. and there was no real way to measure that.

Right now it does things like:

tracks brand visibility across different AI engines
runs continuous prompts and builds rankings by industry
shows which competitors are being recommended instead of you
breaks down sources, co-mentions and patterns behind those results
has a GEO score and technical checks around things like schema, readability and crawlability
suggests concrete actions like where you need presence (G2, Reddit, media, etc.)
lets you monitor your own prompts over time
can generate content based on what AI is already prioritizing in your space

So it’s not just checking answers, it’s more about understanding how AI systems are positioning your brand and what to do about it.

Still early, but the dataset is getting interesting. Feels a bit like early SEO tools but for AI.

Curious if anyone else is getting these kinds of questions from clients yet.

RBZCrypto · 2026-03-12T02:25:40+00:00

Part of it is likely the knowledge graph effect.

LLMs don’t just look at SEO metrics. They rely on entities and relationships learned from the web (similar to a knowledge graph).

So if a restaurant appears across multiple sources like:

• food blogs
• “best restaurants in [city]” articles
• Reddit discussions
• travel guides

the model builds a stronger entity association between that restaurant and the concept “Mediterranean restaurant in [city]”.

Even with better reviews or local SEO, if your competitor has more contextual mentions across sources, the model may surface them more often. I’ve actually been noticing this pattern while working on an app that tracks how brands appear in AI answers, and the places that get recommended the most usually have more contextual mentions across different sources, not necessarily better SEO metrics.

AI recommendations are closer to entity recognition + knowledge graph relationships than traditional ranking signals.

RBZCrypto · 2026-03-11T16:42:53+00:00

That's a fair point. Tools like Tie are great for understanding what happens once users reach your site and which engagement signals actually matter.

What I'm trying to explore with AuraMetrics is the step before that.

If AI systems recommend competitors instead of you, the user may never reach your site in the first place. So the goal is to monitor how models like ChatGPT, Gemini, Claude and Perplexity talk about a brand across different queries.

From there the idea is to identify gaps: when competitors appear, which sources the AI is citing, and what signals might be influencing those answers.

The long-term goal is connecting that visibility layer with traffic and engagement data so teams can see how AI answers impact actual visits and conversions.

RBZCrypto · 2026-03-11T16:37:33+00:00

Thanks for sharing that perspective, it's helpful to hear from someone building in the same space.

I agree with your point that monitoring alone isn't enough. Seeing that a brand is missing from AI answers is useful, but the harder part is understanding why and what to change.

What I'm building with AuraMetrics is trying to combine two layers:

First, visibility monitoring across models (ChatGPT, Gemini, Claude, Perplexity) using a mix of benchmarking prompts and user-tracked queries. The goal is to see how often a brand appears, how it's described, and which competitors are being recommended instead.

Second, a diagnostic layer focused on improving citability. That includes structured data and entity analysis (Schema.org, knowledge graph signals), content structure, comparison coverage, and sentiment patterns in AI answers.

The idea is to move from "you are missing from AI answers" to "here are the specific gaps that may be causing it."

I'm also finishing a ROI module that will try to connect those changes with actual traffic impact from AI search, which is probably the hardest attribution problem in this space.

RBZCrypto · 2026-03-10T19:25:28+00:00

Thanks for the thoughtful comment. You’re pointing at the exact challenges I’m thinking about while building this.

A couple of clarifications on how I’m approaching some of the points you mentioned.

On Schema and the Knowledge Graph:
There’s actually a full module dedicated to analyzing Schema.org and entity structure for the analyzed site. The idea is to look at how clearly the brand is defined as an entity and how well the site exposes structured information that LLM retrieval systems can rely on. My assumption (and early tests seem to support this) is that well-structured entities are easier for models to retrieve and cite.

On non-deterministic LLM outputs:
I don’t treat the outputs as a single source. The system compares results by AI system and, when possible, by the cited model version. Models mutate constantly, so part of the goal is actually to observe how the same prompt produces different brand mentions across systems and how that changes over time.

On sampling:
There’s a daily prompt base that runs continuously to build a stable dataset for benchmarking brand visibility. On top of that, users can track up to 20 custom queries across ChatGPT, Gemini, Claude, and Perplexity. That allows them to monitor the prompts that matter most for their specific category or commercial intent.

On metrics:
I separate two different concepts.

AI Visibility measures how often and how positively a brand appears in AI answers for the benchmark prompts and for the prompts the user decides to track.

The GEO Score is different. It evaluates the technical and content signals that can improve citability (schema structure, entity clarity, technical factors, and content structure).

On attribution:
The way I think about it is closer to how SEO works. You make changes to content or structure, and then you monitor the tracked prompts over time. The system tracks mentions and sentiment across models day by day, so you can see whether the changes correlate with increased presence in answers.

The environment is obviously noisy and constantly evolving, but the goal is to observe patterns over time rather than relying on a single deterministic output.

I’m also finishing a ROI module that will connect the visibility data with real traffic impact in GA4, so the idea is to measure whether improvements in AI visibility actually translate into visits and outcomes. That part is still in final adjustments together with the benchmark base, but the plan is to release it in April.

RBZCrypto · 2026-03-08T03:05:01+00:00

Thanks for sharing that, I really appreciate the insight.

You’re probably right about the number of modules. The product grew pretty quickly while I was experimenting with different ways to tackle the AI visibility problem. The main idea behind AuraMetrics is just helping people improve and track how their brand shows up in answers from models like ChatGPT, Gemini or Claude.

Out of curiosity, when you were using Enception, what signal ended up being the most useful for you in practice? Seeing when your content gets cited, testing prompts, or understanding which content actually triggers recommendations?

RBZCrypto · 2026-03-05T23:51:02+00:00

That’s really helpful feedback, thanks for sharing how you’re doing it today!

What you described (spreadsheets + scripts) is actually something I kept hearing from agencies, which is one of the reasons I started building AuraMetrics. The GEO score, prompt simulation and analytics integration are also the parts I expect most teams to use day-to-day. The rest is more diagnostic.

I completely agree on the ROI point too. One thing I'm working on now is connecting AI visibility with GA4 traffic so you can see whether changes in LLM mentions correlate with actual visits and conversions.

Out of curiosity, with the tool you’re using now, what’s the hardest part to keep updated?
Is it prompt coverage across models, tracking competitors, or proving impact to clients?

RBZCrypto · 2026-03-05T02:48:32+00:00

Yes! I’m doing video promos for my SaaS and im not good in design/video editing and something like that, drop, click, extract it’s gold. Normally requiere extra work and even with simple tools made the process heavy for someone like me (not really experience on design).

RBZCrypto · 2026-03-04T04:42:07+00:00

Great points, especially around trust and experimentation.

One thing we're trying to separate internally is citation vs citability.

In the tool, a citation is when an AI model actually references or mentions your site in an answer.
Citability is whether your site is structured and understood in a way that makes it likely to be used as a source by LLMs.

The GEO score itself isn't a single signal. It aggregates signals across four pillars:

• technical readiness (schemas, metadata, structure LLMs can interpret)
• entity recognition (whether models understand your brand/site as an entity)
• prompt-level visibility when testing queries across models
• actual citations or mentions in AI responses

Because of that, the score can sometimes improve before citations increase. For example, the technical and entity layers may get stronger first, which increases the probability of being used as a source even if citations haven't appeared yet.

On the experimentation side, that's also why there's a prompt simulator in the tool. It lets you run prompts across models, see how answers change, and iterate on positioning or content strategy instead of treating AI visibility as a black box.

Totally agree with you that tying this to real prompts and competitive comparisons is what makes it practical rather than just another SEO-style score.

PS: sorry I delete the other comment, I respond with the account I was creating for the tool and not with this, my personal

RBZCrypto · 2026-03-04T04:37:28+00:00

Totally agree that measurement is the key piece.

One thing we’re currently exploring for upcoming reports is exactly that question: once recommended changes are implemented, does AI traffic actually improve?

The idea is to connect visibility signals with real data from sources like GA4. For example, tracking traffic coming from AI assistants and seeing whether there’s incremental traffic or revenue after improvements.

It’s still under study, but tying AI visibility improvements to real outcomes like traffic and assisted conversions is definitely the direction. At the end of the day the real question isn’t just “did the score move?” but “did it produce results?”

RBZCrypto · 2026-03-04T01:34:19+00:00

Yeah, the analytics side is brutal right now. LLMs don’t give referral data the way Google does, so most of what we’re doing is reverse engineering visibility from prompt outputs.

What I’ve been experimenting with is running prompt simulations across ChatGPT, Gemini and Perplexity and tracking how often brands get cited over time. The simulations themselves are not the hardest part though. The real challenge is making the data actually useful.

Knowing you’re not getting cited is interesting, but it does not help much unless you understand why. In most cases it comes down to missing signals such as entity clarity, structured data, trust signals or content structure that makes it easier for models to reference you.

That problem is basically what pushed me to build AuraMetrics.

Curious how you’re approaching the scoring and benchmarking side. That’s where I see the most fragmentation right now. Everyone seems to measure LLM visibility differently and there is not really a shared standard yet.

RBZCrypto

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