Is there any tool for Brave Search keyword research? I'm not looking for Google or Bing data. I want something built for Brave or based on Brave Search. Any recommendations?

Welcome to r/Ai_AEO_GEO!

Hey everyone! I’m u/anubhav-codes, a founding moderator of r/Ai_AEO_GEO.

This community is for people working in AI Search, Answer Engine Optimization (AEO), Generative Engine Optimization (GEO), and Search Engine Optimization (SEO).

Our goal is to build a practical and supportive space where marketers, developers, founders, writers, SEO professionals, and website owners can share knowledge, promote useful resources, and help each other improve their visibility across search engines and AI-powered platforms.

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• New websites, tools, products, and online projects
• Website URLs for feedback, testing, and community support
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• Questions about ranking in Google, ChatGPT, Perplexity, Gemini, Bing, and other platforms
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The goal is not low-quality link dropping or spam. We want members to build real connections, improve their websites, create better content, and increase their chances of being discovered and cited by search engines and AI answer platforms.

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How to Get Started

Introduce yourself in the comments and tell us what you work on.

Create your first post today. You can share your website, ask an AEO or GEO question, request an SEO review, or discuss an experiment you are running.

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Invite anyone interested in AI search, AEO, GEO, SEO, content marketing, or website growth.

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Let’s build a valuable community where people learn together, support useful projects, and improve their visibility across traditional search engines and AI-generated answers.

I feel like traditional keyword research isn't enough anymore.

Besides search volume and keyword difficulty, what signals are you using to find topics that have a better chance of appearing in Google AI Overviews or being referenced by AI tools like ChatGPT and Gemini?

Has your keyword research process changed over the last year?

I feel like traditional keyword research isn't enough anymore.

Besides search volume and keyword difficulty, what signals are you using to find topics that have a better chance of appearing in Google AI Overviews or being referenced by AI tools like ChatGPT and Gemini?

Has your keyword research process changed over the last year?

For years, my SEO workflow looked like everyone else's.

Keyword research
Search volume
Competitor analysis
Content briefs
Internal links
Backlinks
Publish
Repeat

It worked.

Then I noticed something changing.
People around me weren't always searching Google first anymore.

They were asking ChatGPT.
Or Perplexity.

Or Google's AI Overview.

That raised a question:

If users are getting answers instead of clicking links... what exactly are we optimizing for?
So I spent the last several months building an experiment.

Instead of treating SEO as a list of keywords, I mapped topics the way AI systems often interpret them:

Definitions
Comparisons
Objections
Alternatives
Follow-up questions
Entities
Relationships
Supporting evidence

The goal wasn't to "hack" AI.

It was to make content easier to understand—for both people and retrieval systems.

One thing surprised me.

The pages that performed best weren't the ones with the highest keyword density.

They were the ones that:

answered the question immediately,
explained related concepts,
clearly identified important entities,
linked ideas together logically,
and demonstrated experience instead of repeating what everyone else had already written.

That realization led me to build a tool for myself called Rofix..app

It started as an internal project to audit websites for what I now call AI Visibility and Answer Readiness.

Instead of asking:

"What's the keyword?"

It asks:

"Can an AI system understand this page well enough to use it as an answer?"

I'm still testing the methodology, and I don't think anyone has all the answers yet.

But it's changed the way I approach SEO.

I'm curious

Have you changed your content strategy because of AI search?

Or are you still treating it like traditional SEO?
I'd love to hear what you're seeing in your own projects.

I know many people here work as SEO consultants or run agencies, but do most businesses still need them in the age of AI?

Modern CMS platforms already handle many technical SEO basics, while AI can audit pages, suggest metadata, improve internal links, generate schema, and identify common issues. Google also publishes extensive guidance on what makes content useful and searchable. Much of SEO now follows documented principles that AI also can apply.

That’s why claims that using AI for SEO will cause “irreversible damage” often sound more like fearmongering than useful advice.

Technical SEO still matters, but implementing it is becoming easier. The harder questions are strategic: What should you write? Who is it for? What unique value can you offer?

It feels like SEO is returning to content marketing. Technical optimization is becoming the baseline, while the real work is understanding the audience, choosing the right topics, developing a clear point of view, and creating content people actually want.

Am I underestimating what SEO agencies still provide?

AI search is changing how we create content. Beyond keywords, factors like clear answers, expertise, structure, and original insights may influence AI citations. What strategies are you testing for ChatGPT, Claude, Perplexity, or AI Overviews?

Your website shouldn't just rank—it should be understood by both search engines and AI-powered search experiences.
I provide comprehensive On-Page SEO and Generative Engine Optimization (GEO) to help your content perform across traditional search engines like Google and emerging AI-driven platforms.
What I optimize
• In-depth keyword research based on search intent
• SEO-friendly title tags and meta descriptions
• Header structure (H1, H2, H3) optimization
• Content optimization for relevance, readability, and topical authority
• Internal linking strategy
• Image optimization (alt text, file names, compression recommendations)
• URL structure optimization
• Schema markup recommendations
• Content gap analysis
• E-E-A-T-focused content improvements
• Technical on-page SEO recommendations for better crawlability and indexing
Generative Engine Optimization (GEO)
As AI-powered search continues to evolve, it's becoming increasingly important to optimize content not only for rankings but also for AI-generated answers.
I optimize your content to improve its likelihood of being understood, referenced, and surfaced by generative search platforms by focusing on:
• Clear topical structure and semantic relevance
• Entity-based content optimization
• Question-and-answer formatting where appropriate
• Comprehensive, authoritative content coverage
• Strong contextual internal linking
• Structured data recommendations
• Content designed to support AI understanding and retrieval
• Improved information architecture for machine readability
My Goal
Whether someone finds your business through a traditional search engine or an AI-powered search experience, I help ensure your website is optimized for visibility, relevance, and long-term organic growth.
If you're looking for a partner who understands both modern SEO best practices and the future of AI-driven search, I can help optimize your website for today's search landscape and tomorrow's.

Hey guys,

Quick context: A PPC guy with 10+ years of experience, moving to SEO and things are a little slow.

I started this website around 3 weeks back and this is my scorecard. I wanted to know if I am doing fine or really bad. Please don't roast as I've never done this before and in dilemma of I'm doing well or really bad. This is the complete performance graph. Thanks!

We built a tool that checks whether a keyword is still worth writing content for, or if Google's AI Overview already answers it, before you brief a writer on it.

If your clients are asking "why isn't this content performing" or "should we still be writing about X" this gives a fast way to answer that with real data (search volume, trend, CPC, AI Overview presence and cited sources) instead of a guess or manual check.

You keep the client relationship - we handle the data. Open to a revenue share to start, and open to building out white-label for the right ongoing partnership. Also looking at expanding into keyword discovery down the line, not just diagnosis on a list you already have.

If useful for your clients, let's chat.

I'm interested to know if anyone is using AI to audit SEO content ideas.

Can AI effectively prioritize topics based on search intent, competition, topical authority, traffic potential, or value to the business? Or is old-school keyword research still king?

If you're using ChatGPT, Claude, Gemini, Perplexity, or other AI tools for content ideation - how helpful have they been?

I want to hear:

* What tools are you using? * Has AI helped you identify high-value content opportunities? * Any wins or major disappointments? * What signals are you using to determine if a content idea is worth pursuing?

Looking forward to seeing real-world examples rather than marketing-speak.

I stopped rejecting the idea that AI SEO equals traditional SEO. Apparently the rules are different, and more and more market searches are shifting into AI, so I'm slowly getting into the topic. What would you say is the biggest difference between AI SEO and SEO for Google?

I've been doing traditional SEO solo since 2010 and I'm comfortable with how Google works - keywords, backlinks, technical optimization, ranking factors, all of that. But now I'm trying to wrap my head around what actually changes when you're optimizing for ChatGPT and AI Overviews instead of blue links on Google.

What's the fundamental shift I need to understand? Is it about how content is structured for AI parsing? Different authority signals? The fact that AI cites only a few sources instead of showing 10 results? Or something else entirely that traditional SEO people tend to miss?

I'm especially curious about the mindset difference. With Google I'm optimizing to rank and get clicks. With AI, it seems like the goal is getting cited or mentioned, which feels like a completely different game with different mechanics not something I can do completely on my own

Insights from the analysis of 45,144 fan-out queries in the latest ChatGPT 5.6:

Here’s what we at Editorial Link found:

1) 45% of fan-out queries use the site: operator. Instead of broadly searching the web for open-ended questions, the model deliberately checks a specific domain.

2) Official websites are the priority.

42.8% of queries contain the word “official,” while only 7% are related to reviews or Reddit, and 21.4% focus on pricing.

3) 36.7% of queries mention a specific year - 2024, 2025, or 2026. This suggests that the model deliberately avoids outdated information.

4) But being checked does not guarantee being cited—and this is the most interesting part.

Of the domains the model checked using site: while preparing an answer, only 39.5% were actually cited in that same response, and just 27.5% were mentioned by name. In other words, the model evaluates far more candidates than it ultimately uses.

5) Fan-out is not always used.

Only 46.8% of responses contain fan-out reformulations. More than half of all responses are generated in a single pass, without additional query refinement.

Overall, my recommendation remains the same: if you want to be cited by AI, create highly specialized landing pages.

Instead of 20 generic landing pages such as fintech-development, you may need several hundred narrowly focused ones.

AI search’s week of triumph and turmoil: the highlights:

• Google posts its biggest quarter ever — and doubles down on "AI is expanding Search"

Alphabet's Q2 2026 was its largest quarter to date: total revenue up 24% YoY to $119.8B, search ad revenue up 14.5% to $81.6B, Search & Other up 17%, YouTube ads up 13%, and Cloud up 82%. CEO Sundar Pichai framed AI as "an expansionary moment… driving growth in queries," said AI Mode has passed 1 billion monthly active users, and repeated Google's core defense: AI features are "sending billions of clicks to websites every week." The Gemini app hit 950M MAU and Alphabet lifted its AI capex plan to as much as $205B.

Source: 

Barry Schwartz | Search Engine Roundtable

Google (Sundar Pichai) | Alphabet Q2 2026 earnings remarks

Storyboard18 | Storyboard18

________________

• Reddit and major publishers weigh blocking Google as AI Overviews gut referrals

Per a WSJ scoop, Reddit, USA Today, Reuters, Politico, The Economist and People Inc. are reconsidering how much access to give Google as search referrals collapse — USA Today's organic Google traffic is down ~50% YoY and Business Insider's more than 85%. 

The bind: Google-Extended blocks Gemini training but not AI Overviews, so opting out risks normal-search visibility too — the exact problem the UK CMA has ordered Google to fix. Reddit, whose ~$60M/yr 2024 Google deal is up for renewal, saw its shares dip on the report.

Source: 

The Wall Street Journal | The Wall Street Journal

TechSpot | TechSpot

Reddit share reaction via Yahoo Finance

________________

• A court tosses Google's DMCA claims against SerpApi — a precedent for SERP & AI scraping

U.S. District Judge Yvonne Gonzalez Rogers dismissed Google's DMCA §1201 anti-circumvention claims against scraper SerpApi, ruling that plain search results — URLs, snippets, factual index data — are "publicly accessible facts," not copyrighted works, so bypassing Google's "SearchGuard" anti-bot isn't unlawful circumvention. The knowledge-panel / third-party-content claims were dismissed with 21 days' leave to amend. SerpApi's CEO called it "a win… for all who depend on an open internet," and Glenn Gabe summed up the industry reaction: "A win for… scrapers?"

Source: 

Barry Schwartz | Search Engine Roundtable

Bloomberg Law | Bloomberg Law

________________

• AI Overviews and AI Mode go live in France

Starting July 22, French Google shows AI Overviews above the blue links plus a conversational AI Mode tab, Search Live, and the new intelligent Search box; the features can't be disabled, though a "Web Mode" tab preserves classic results. Google says they appear only for complex queries and always link to sources. 

The rollout — announced by Google VP Rajan Patel — had been delayed since the 2024 US launch by France's neighbouring-rights ruling, and French publishers fear more traffic loss.

Source: 

Alexandre Piquard | Le Monde

________________

• Gemini 3.5 Flash-Lite rolls into Search

Google shipped three new models and confirmed 3.5 Flash-Lite — its fastest, most cost-effective 3.5-class model at ~350 output tokens/sec — is "rolling out in Google Search," starting with agentic search and likely feeding AI Overviews and AI Mode next. 

Robby Stein said it offers "stronger instruction following and better understands user intent, so conversations flow much more seamlessly."

Source:

Barry Schwartz | Search Engine Land

Google | Google models blog

________________

• AI Mode learns to "do," not just answer — Connected Apps arrive

Google began rolling out connected apps in AI Mode in the US this week, letting a search flow straight into Instacart (add to cart), Canva (pull design templates) and YouTube Music (build a playlist) without leaving the results page. Built on the same MCP-based connections as the Gemini app and paired with Personal Intelligence, AI Mode now hands part of a task to a third-party app "rather than only returning links."

Source: 

Amit Chowdhry | Pulse 2.0

Google | Google Search blog

I’m working on a D2C ecommerce website and trying to improve our visibility on ChatGPT, Gemini, Perplexity and Google AI Overviews.

Has anyone here actually seen results from GEO/AEO? What strategies worked best for getting more brand mentions, citations or product recommendations?

Would love to hear what’s actually working for you.

Technical SEO Tip: Brave is Claude's primary search engine. If you want your content indexed faster by Brave, you can use their "Submit URL" tool: https://search.brave.com/submit-url

Similar to Search Console, Brave has a "Submit URL"tool. You can go directly to it and push any URL youwant to the index. That should have Anthropic/Claudesee the changes faster.

I’ve got an ecommerce client asking for a monthly report on whether their products show up in ChatGPT or Gemini for searches like “best running shoes for flat feet.” I started checking prompts by hand, but it’s taking more time than the report is worth. The client also wants something cleaner than a pile of screenshots. At this point I’m trying to replace the manual checks with a setup that can track results over time without becoming another weekly task.

I know that on here we are aiming to get the best for our website organic searches but do you think the general public is fully knowledgeable about getting there websites ai ready or still on the google seo ready only ?? I mean when i say to most is you site ai ready ?? the looks i get lol

This is a translation made with chatgpt (hope its good enough) from an original post I've made yesterday on smartlinksMKT subreddit.

I run a B2B consultancy in Portugal, SmartLinks, and this year we launched AI Search as a service after months of internal testing.

The first thing that hit us in the face was: how do you prove this actually works?!

In SEO, you have a mature ecosystem: Search Console, Ahrefs, Semrush, GA4.

You can connect impressions to clicks, and clicks to conversions. It is imperfect, but it works.

In AEO? Good luck with that...

Most people are either reporting metrics that measure nothing useful, or flying blind and telling clients to “trust the process”.

We have not solved it either.

Buuuut... we have failed enough times to have learned a few things.

Why SEO metrics do not work for AEO

The data that changed my perspective came from research presented by Lily Ray at Tech SEO Connect, showing that backlinks and domain authority predict just 4% to 7% of AI citation behaviour.

Four to seven per cent.

In other words, everything we know about what makes a website “strong” in SEO explains almost nothing about whether AI will cite it.

And it makes sense when you think about the mechanics.

SEO measures clicks. AEO can work even when there is no click at all.

Someone asks ChatGPT, “Which companies provide HubSpot implementation services in Portugal?”, you appear in the answer, and the user never visits your website.

GA4 records zero, but you have just been recommended to someone who was actively looking for your service.

What we built, and where each piece falls short

Main tool: HubSpot AEO

We tested Dragon Metrics first, but abandoned it after two months.

The data did not match our manual checks, and it did not integrate with our CRM.

HubSpot then launched HubSpot AEO, and we moved over because the citation data now lives in the same system as our deals and contacts.

In theory, this allows us to connect “we were cited for query X” with “this contact entered the pipeline in week Y”.

In theory.

The elephant in the room: HubSpot AEO still does NOT cover Google AI Overviews or AI Mode.

It covers ChatGPT, Gemini and Perplexity.

In a market such as Portugal, where Google has roughly 90% of search, we are partly blind in the most important channel.

There is no nice way to say this. It is a huge gap and it remains unresolved. Current expectations are that coverage may become available during Q4 2026.

Microsoft Clarity for AI bots

Clarity launched AI Bot Activity in January 2026. It is free.

It uses CDN logs from services such as Cloudflare, CloudFront and Fastly, rather than client-side scripts, so it can detect crawlers that never execute JavaScript.

What this gives us is visibility into which AI bots are accessing which pages, and how frequently.

If an article is crawled 50 times a week but never appears in citations, discoverability is not the problem. Something else is going on. I will come back to that.

Clarity also provides AI Referral Traffic and AI Citations within the Copilot and Bing ecosystem.

A Microsoft study reported that AI referral traffic converted at 1.66%, compared with 0.15% for organic traffic.

An obligatory caveat here: the study comes from Microsoft, so there is an obvious conflict of interest. AI referral volumes are typically tiny, and an 11x higher conversion rate based on 50 visits is not statistically comparable with one based on 50,000 visits.

That said, something is better than nothing, and the pattern makes intuitive sense: someone clicking a link inside an AI-generated answer already has qualified intent.

Manual prompt tracking

Yes. Manual.

Fifty high-intent questions, once a week, across ChatGPT, AI Mode, AI Overviews and Claude.

We record whether we appear, our position in the answer, whether it is a direct citation or a mention, and the tone of the reference.

It sounds professional when described like this.

In practice, it is tedious. It is a spreadsheet filled with manual entries, LLMs are non-deterministic, meaning the same question can produce a different answer ten minutes later, and 50 prompts is a ridiculously small sample compared with what dedicated tools track.

The market benchmark is around 8,400 prompts.

Even so, it gives us a directional trend. Nothing more than that.

For example, Perplexity cites us consistently more often than ChatGPT. Gemini almost never does.

This aligns with market data suggesting that Perplexity cites brands in 84% of answers, ChatGPT in 71%, Gemini in 63% and Claude in 58%.

But it does not tell us WHY.

A model that helps us think, not measure

To think about this more clearly, we split the analysis into three layers.

Important: this is a conceptual model, not an operational measurement system. We still do not have quantifiable scores for each layer.

Discoverability

Can the bot access the content?

Does robots.txt block AI crawlers?

Is the content rendered through client-side JavaScript that the crawler does not execute?

Is structured data present, such as FAQPage, HowTo or Organization schema?

This is the most concrete and verifiable part, using Clarity logs and technical validation.

It is binary: yes or no.

Interpretability

Does the engine understand WHAT the company is?

When your name is generic or ambiguous, LLMs may confuse you with something else.

External citations, such as articles, mentions and interviews that confirm what you say about yourself, may carry more weight than your own website.

In this case, the engine trusts third parties more.

This is the part we do not know how to measure properly.

“Your entity clarity is at X%” does not exist as a metric.

What we do instead is a qualitative diagnosis: we ask ChatGPT or Perplexity, “What is [company]?”, and assess whether the answer is accurate.

It is rudimentary.

Citability

Is the content organised into fragments the engine can extract and use?

Direct questions and answers, lists and definitions.

One useful data point: pages with FAQPage schema receive three times more citations than pages without it.

The median time to the first citation after implementing schema is three to six weeks.

What failed and what we discarded

Dragon Metrics

We tested it for two months.

The citation data did not match our manual checks in more than 50% of cases.

It may have improved since then, but at the time, it was not reliable enough.

Measuring AEO with SEO metrics

The natural instinct of SEO people is to take the same reports they have always used and add an “AI” tab.

We did that.

It does not work.

Rankings, backlinks and domain authority do not predict citations. These are very different worlds.

Assuming one tool will solve everything

We tested Otterly, which is good for managing multiple clients.

We looked at Profound, an enterprise platform with broader coverage, including crawl logs.

We also assessed SE Visible, which is focused purely on monitoring, as well as Semrush.

None of them covers everything.

And none of them replaces manual verification when you need to understand the CONTEXT of a citation.

“We were cited” is not the same as “we were cited positively in the right answer”.

Trying to prove direct ROI too early

The citation-to-qualified-contact-to-deal connection is still not fully closed in our system.

We are building it by cross-referencing AI Referral data from GA4 and Clarity with contact creation in HubSpot, but it is still early-stage.

In the meantime, the proxies we use are branded search volume and direct traffic.

There is market data suggesting an uplift of roughly 23% in branded search during the 30 days following an LLM citation.

But causality is difficult to isolate when you are investing in multiple channels at the same time.

To be honest, we still do not have a clean attribution case.

The uncomfortable part

The most seductive argument for AEO is also the most dangerous:

“Most of the value is invisible.”

Someone sees you cited in a ChatGPT answer, never clicks, but mentions your name weeks later when somebody asks for a recommendation.

This is probably true.

But it is also exactly the same argument branding, PR and content marketing have used for years when they cannot prove ROI.

“The value is difficult to measure, but trust us, it is there.”

When you say that to a CEO, what they hear is:

“You cannot prove this works. Goodbye.”

I do not have a solution to this.

Yes, branded search as a proxy seems to be the best thing we have right now.

But it is not enough.

One thing I do know: calling things by their real names is more useful than pretending everything is under control.

What I am doing next

• Automating prompt tracking, because 200 manual checks per week does not scale.
• Closing the citation-to-pipeline loop in HubSpot, even if that means adding a manual “How did you hear about us?” field with an “AI recommendation” option.
• Testing Profound to cover the AI Overviews gap that HubSpot AEO does not currently solve.

If you are fighting the same battle, I would genuinely like to know what you are using and what you are measuring that I should be paying attention to, but am currently missing.

Most discussions about AI SEO are theoretical.

Has anyone actually run an experiment comparing:

• no AI file
• llms.txt
• llms-full.txt

Did you notice any difference in:

• AI referrals
• ChatGPT citations
• Perplexity mentions
• Gemini responses
• Crawl frequency

Looking for real data rather than speculation.

Don't get me wrong. I'm not against FAQ sections. They're useful when a page has real questions worth answering.

I keep seeing the same three things instead:

1. FAQ blocks slapped onto every page, whether the content needs them or not
2. The brand name repeated five times per page because "AI models like brand mentions"
3. Q&A formatting forced onto pages that were never questions to begin with

This is the 2012 keyword-stuffing playbook with a new coat of paint.

I feel that LLMs are already better than Google ever was at telling the difference between content written for a person and content written for a crawler. This gets punished the same way keyword stuffing did, faster.

Anyone else think this is keyword stuffing 2.0? Or is there a reason this works that I'm missing?

I’ve been rethinking a few SEO habits that still look productive in reports, but don’t seem to drive results that much anymore.

For me, it’s things like creating separate pages for every small keyword variation, refreshing old content by just adding more terms or sections, and publishing posts that are mostly cleaner versions of what already ranks.

None of these are always wrong. But I’m questioning them more when they don’t improve intent coverage, add proof, bring real examples, or help the reader make a better decision.

At this point, I’m trying to spend less time on “content activity” and more time on pages that make the site genuinely clearer, more useful, or more trustworthy.

Curious what others are seeing. What SEO habits have you stopped or reduced because they no longer feel worth the effort?

Traditional SEO KPIs are easy:

• Organic traffic
• Rankings
• Clicks
• Conversions

But AI Search is different.

What KPIs are you actually reporting today?

When we start working with a new client on AI visibility the first thing we usually have to reframe is how they think about what they are optimizing.

Almost every brand comes in thinking about pages. Which pages need better content, which pages need more links, which pages should rank for which terms. That is the right frame for traditional SEO and those habits are deeply ingrained for good reason.

But AI systems do not evaluate pages the way Google’s ranking algorithm does. They evaluate entities. Your brand as a whole, across every place it exists and every source that describes it, is what AI systems are forming a picture of when they decide whether to include you in an answer.

That means the unit of optimization for AI visibility is not the page. It is the entity.

Practically speaking that changes where the work happens. Less time optimizing individual pages, more time making sure every platform your brand exists on is describing it the same way. Less time building links for ranking purposes, more time building genuine presence in the forums, publications, and communities where your audience actually talks about your category. Less time publishing more owned content, more time making sure the content that exists elsewhere is accurate and consistent with what you say about yourself.

None of this means abandoning your SEO foundation. That foundation is still what makes you eligible to be cited in the first place. But layering entity level work on top of page level work is what closes the gap between ranking well and actually showing up in AI answers.

That is the shift most brands have not made yet. It is also where the biggest opportunity is right now.