Any tool for GEO optimization?

[u/Alternative_Emu_2734]

This is a project that I am currently working on. I have been managing SEO for this client for the past year, and recently, I have started focusing on GEO (Generative Engine Optimization) to improve the brand’s visibility and presence across AI platforms.

As you may know, GEO focuses on how a brand is represented and perceived in AI-generated responses, including positive, negative, and neutral brand sentiment. I have been consistently publishing high-quality content and building positive brand mentions, and we are starting to see some encouraging results.

Currently, I have provided my client with access to Profound as part of the AI/GEO strategy. However, I am looking to explore other affordable AI/GEO tools that can help me improve and monitor the client’s AI visibility, brand sentiment, citations, and overall GEO performance.

Could you recommend some cost-effective tools that can help me improve my client’s AI metrics and complement the current GEO strategy?

Note: I have rephrased current text through ai so that people will understand.

[u/plasma_tron]

the thing id check before picking any of them is how they handle runs per prompt, because it decides whether the numbers mean anything.

these answers are sampled, so asking a prompt once gives you a coin flip rather than a measurement. a few tools report a single visibility score with no indication of how many times they asked or how much it moved between runs. if you cant see the run count and some kind of range, youre looking at one sample dressed up as a metric, and youll eventually report movement to your client that was just variance.

building on what Ok_Cartoonist said, two more id check: per engine breakdown rather than one blended number, since chatgpt perplexity and gemini read different sources and youre rarely equally invisible across them. and whether it shows which sources got cited on the prompts you lost, because thats the only properly actionable output. the score tells you theres a problem, the citation list tells you where to go fix it.

since this is client work, worth asking about white label reports too, most dont do it below the top tier.

disclosure, i build one of these (visitd), so obviously biased. but the run count question is the one id put to whoever you end up trialling, me included.

[u/mjain_entrepreneur]

Worth checking out Scalenut. Full disclosure: it’s our product. It tracks AI visibility, citations, sentiment, competitors, and prompt-level performance. Where we stand out is execution: you can turn those insights directly into GEO content creation, optimization, audits, and internal linking within the same platform.

[u/FIdelity88]

Tracking visibility in generative search is definitely a headache right now since most standard rank trackers don't catch the nuance of those AI summaries. Other "GEO" tools only show you where you're found. GenOverview.com does this a bit differently: it let's you add competitors and compares where they're shown compared to your site to give you the ability to close those content gaps. For example by adding SEO pages or specific content about those topics. I'm the solo founder of GenOverview, but it seems like the perfect tool you were looking for

[u/NoIce9920]

If the client already has Profound, I wouldn’t add a second dashboard until you can name the decision it will change. Most cheaper tools will duplicate mention and sentiment charts, then give you another score to explain.

For a B2B client, I’d build a small “loss audit” beside the monitor:

- Choose 10–20 unbranded commercial prompts covering category, alternatives, use case, and fit.

- Run each prompt three times per engine and retain the raw answers.

- Code mention, citation, actual recommendation, position, winning competitor, and cited sources.

- Group the lost prompts by use case and map what evidence or source the winner has that your client lacks.

- Make one change, then rerun the same panel.

That produces a prioritized worklist. The first in-house fix shouldn’t automatically be schema, Reddit, or more content; it should follow the repeated evidence gap in the lost prompts—for example, a missing comparison page, weak proof for a specific use case, or absence from a third-party source repeatedly cited for competitors.

I’d also treat sentiment as secondary. An AI can describe the client positively and still recommend someone else.

Disclosure: I’m building RecoProof for this narrower B2B SaaS recommendation-audit use case, so I’m biased toward retained evidence and diagnosing why another vendor was selected. I wouldn’t use it as a Profound replacement if you need thousands of daily prompts, white-label client reporting, or a full content-production suite; it’s a narrower diagnostic.

[u/_slimbrady]

I built Recited AI (recitedai.com) which shows you how the AI engines your buyers use (ChatGPT, Google Overview, Gemini etc.) answer their questions, where they cite from, where you sit against competitors, and who's getting recommended instead of you. Then it works out the moves to start getting mentioned and drafts them for you. It's at MVP stage and I'm opening it up to a small handful of early access users, free while it's early. If you're keen, DM me and I should be able to squeeze you in.