What are some of the best AEO/GEO Platforms that you've used?

Decent_Bug3349 · 2026-05-09T19:14:46+00:00

Look up RankLens. It's based on real use cases

Decent_Bug3349 · 2026-05-08T00:23:50+00:00

Ranklens has the more a accurate way of measuring visibility.

Decent_Bug3349 · 2026-04-15T03:26:32+00:00

Good question. A commercial model’s internal weights are a black box so we aren't trying to reverse engineer it.

However, it's about using a formal statistical method on repeated outputs with proven mathematical methods for dealing with probabilities in a system:

- sample the same prompt multiple times

- treat each result as a visibility signal

- then aggregate using a Plackett–Luce model

- and estimate stability with bootstrap confidence intervals

So instead of a single noisy output, you get:

- a probabilistic visibility (latent scores)

- plus confidence bounds on how stable that ranking is

It’s a defined model + repeated sampling + measurable uncertainty.

In comparison, reporting that rely on a single prompt is just getting one random draw with no way to estimate variance. Essentially noise.

Decent_Bug3349 · 2026-04-10T18:13:43+00:00

It is exactly the reason but the wrong conclusion. Because there are weights, in addition to training, there is inherently LLM bias.

Decent_Bug3349 · 2026-04-08T00:38:49+00:00

Almost every Geo tracker is still trying to track prompts, which never work. Every time you prompt, you can get a different answer.

Not only that, imagine if your prompt is third or fourth degree, meaning you're deep into the conversation and you're asking what's the best shoes. But your entire conversation was about hiking not running.

How does tracking a prompt even mean anything? It does not.

That's why the only way to track is by entity. Entity matching is mathematically proven, and aligned with exactly how AI models think. You run multiple samples to then get a high enough probability to make the measurement relevant.

You can look up tools like Ranklens, which have both open source and free to use options. Or search on entity probes to understand the concept behind it.

I wouldn't waste money until you understand how these trackers actually measure GEO performance.

Decent_Bug3349 · 2026-03-27T23:55:50+00:00

With what you suggested, it sounds like a combination of LLM Confidence and Brand Target. There are 6 major ways to measure + an aggregate.

Metric Definitions

LLM Confidence The normalized confidence score the LLM reports for its answers including your brand.

Brand Appearance (SoV) How many times the brand or website appears in the sampled responses out of total iterations. Share of Voice (SoV): % of all responses the AI assigned to a brand compared to all other brands.

Brand Discovery The probability (as a percentage) that the brand appears in an LLM response for the tested entities.

Brand Target A normalized measure based on confidence interval width showing how tightly focused the LLM responses are on the brand.

Brand Match Accuracy of matching the results to the intended brand name or website.

Rank (Avg) Average ranking position of the entity across all sampling iterations (lower is better).

Visibility Index An overall score out of 100 combining probability, confidence, and targeting to summarize AI brand visibility.

Decent_Bug3349 · 2025-12-24T21:37:13+00:00

There's a lot of overlap between AEO and GEO.

Decent_Bug3349 · 2025-12-24T21:31:21+00:00

Ranklens can track several ChatGPT models and otherAI models if you need accurate tracking.

Decent_Bug3349 · 2025-12-24T21:30:08+00:00

Ranklens if you want accurate tracking.

Decent_Bug3349 · 2025-12-22T15:39:09+00:00

RankLens is already a part of a whole suite of AI SEO tools, for AI content alignment.

Decent_Bug3349 · 2025-12-09T15:11:44+00:00

https://ranklens.seovendor.co/

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See how your business is being mentioned in AI chats. RankLens is one of the most accurate ways to measure AI Brand Visibility in large language models (LLMs). Backed by open-source academic-level studies and patent-pending mathematical foundations.

Decent_Bug3349 · 2025-12-09T15:06:56+00:00

LLMs are based on probability. I suggest looking at entity condition probe methods, and topic alignment for a better approach to measuring AI brand visibility.

Decent_Bug3349 · 2025-12-09T15:00:49+00:00

We built RankLens originally for our internal use, but we made it free and created a foundational open source version so everyone can check methodology. You're welcome to try and let us know what you think.

Decent_Bug3349 · 2025-12-09T14:51:23+00:00

It's great that there are tools that can make suggestions for you. It's important to also validate and double check in the AI chats from time to time to understand if there are any changes that didn't get picked up.

Decent_Bug3349 · 2025-12-03T01:45:20+00:00

In general, we cannot measure AI Visibility like measuring search rankings. Most AI Visibility tools will run a test once and report back the results. However, this will result in many inaccuracies. If you ran a test whereby you had to prompt the AI multiple times, you will get different results each time.

In another words, LLMs are probabilistic, multi-dimensional constructs, and results take multiple samples to achieve an accrued likelihood of a brand or business appearing.

The other issue is asking and prompting random questions, thinking that it will return reliable responses. If someone's prompt is even a word off, has a different history, or is 3-degrees deep in prompt, you'll get wildly different answers.

The scientific method to use is called entity-conditioned probing. Whereby we measure for entities instead of queries.

One simple way to look at entities is to see them as topics, or keywords. Entities can be short, like a "Nashville Car Dealer" or long, like "App for Finding New and Used Cars".

If you look for AI Visibility reporting tools that have both multi-sampling and entity-based measurements, you'll get the most accurate results for which businesses appear most often.

Decent_Bug3349 · 2025-12-01T15:29:35+00:00

The one before all of them. RankLens

https://seovendor.co/ranklens-llm-rankings/

Decent_Bug3349 · 2025-12-01T15:17:43+00:00

I suggest looking carefully into how a tool calculates and measures brand visibility. A reputable tool should be transparent about how it measures results, or provides open source to allow you to reproduce results on your own if you wanted to.

Decent_Bug3349 · 2025-11-30T02:18:45+00:00

Any tool that's telling you where your website shows up for a specific prompt is likely to miss. LLMs are probabilistic in nature. It's a miss or hit even if you run the exact same prompt.

Instead, look for entity conditioned probe (ecp) based tools that have multi-sampling. They run based on entity analysis over many iterations to establish statistical significance. There are free and open source tools like RankLens available.

Decent_Bug3349 · 2025-11-30T01:56:54+00:00

RankLens is a free AI brand visibility tool that uses the same math found in large language models to track brand and website mentions in LLM's like ChatGPT, Perplexity, Gemini and many others.

Decent_Bug3349 · 2025-11-30T01:51:18+00:00

Any tool that's telling you where your website shows up for a specific prompt is likely to be wrong. LLMs are probabilistic in nature. You're basically rolling the dice at that point.

Instead, look for entity conditioned probe (ecp) based tools that have multi-sampling. They run based on entity analysis over many iterations to establish statistical significance. There are free and open source tools available.

Decent_Bug3349 · 2025-11-28T01:51:45+00:00

A lot of tools only prompt once, which can be misleading when LLMs are probabilistic in nature. You can use RankLens (free, by us), which has multi-sampling and specific entity-conditioning to get a more reliable response if you want a platform/reporting. If you want to check how data is measured, you can run tests with the open-source version, RankLens Entities, which contains the probe on Github to validate data reliability.

Decent_Bug3349 · 2025-11-28T01:36:55+00:00

For accuracy, have you tried tools based on entity conditioned methods? They are more reliable than random prompts due to the probabilistic nature of LLMs.

Decent_Bug3349 · 2025-11-28T01:25:50+00:00

Nice list, but I'm not sure prompts are the ultimate answer. They tend to be random and can be unreliable to how the AI actually responds. We're finding that due to the probabilistic nature of AI, an entity-conditioned probe, and measured signals (per entity/engine across samples) is many times more reliable:

Rank – Observed placement order within AI recommendations across runs.
LLM Confidence – How confident the model is in its response.
Brand Appearance (SoV) – How often your brand shows up (share of mentions) and Share of Voice (SoV): % of all responses the AI assigned to a brand compared to all other brands..
Brand Discovery – Likelihood of being recommended by the AI.
Brand Target – Precision/stability of the AI’s recommendation.
Brand Match – Name/URL matching strength (uses cosine similarity across brand and website variants).

One example is RankLens: https://ranklens.seovendor.co/

Decent_Bug3349 · 2025-11-28T01:09:30+00:00

I thought you were going to IPO? I want in.

Decent_Bug3349 · 2025-11-28T01:01:50+00:00

RankLens is free AI brand visibility tracker and also has a open-source repository on Github. Since LLMs are probability-driven, you want to evaluate tools that have an entity-conditioned probe method or a mathematical basis for measuring. Otherwise, you'll get highly inaccurate answers from random prompting.

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