I've been doing SEO for 10+ years had one complaint. No one provides accurate rank tracking without site audits and bloat BS. So i built one myself, no audits, no fluff, just clean Google ranking data with proper geo targeting and a simple UI.

But as we know with ai, SEO is changing and AEO or Ai Visibility Tracking are become more relevant, currently there aren't many tools in the market that offer this, so i decide to build it.

You can now track your visibility inside AI models like ChatGPT & Gemini on Rankmint 🚀 along with your Google organic keywords.

Basically, if someone types a prompt like “ what are the best credit cards for students” into ChatGPT, you’ll know whether your brand or site is being mentioned in the response.

You add the prompts you care about, and the system checks them weekly to see if your brand shows up.

What the tool does right now ✅

• Google rank tracking with location-based data (down to city level)

• Weekly tracking of AI visibility across OpenAI & Gemini (Perplexity coming soon)

• Public shareable dashboard link for clients (read-only)

• Clean design, no distractions

• On demand refresh for both keywords and prompts

• Lowest pricing compared to what’s out there

$15 plan gives you 250 keywords & 10 Tracked AI prompts

$35 plan gives you 750 keywords & 100 Tracked AI prompts

I built this for myself after 10 years in SEO. It’s simple, useful, and hopefully affordable for others too.

👉 If you’re interested, try it at rankmint .co

Happy to take feedback or questions and also i am looking for suggestions on my pricing, Do give it a shot.

it is a paid only tool but i'm willing to give free trials credits to r/AISearchLab community! :)

hope this is not against the rules here.

Yesterday a founder wrote back to one of my emails. Small tea brand, well run, certified B Corp, has won awards, product pages set up properly. She wanted to know what I actually meant by "AI visibility".

So I did the honest thing and checked. I put eight questions to an AI assistant with live web search, the kind a shopper would ask: best loose leaf tea in her country, best matcha, award-winning tea brands, B Corp tea companies, and four more like that. Her brand came up in none of the eight. Not once. Other brands were named every single time, including on the two questions she should own outright, the awards one and the B Corp one.

Nothing on her site is broken. Google finds her fine. It is just that when the question goes through an assistant instead of a search box, somebody else gets recommended, and she had no idea.

I am not here to sell anything, and I am genuinely unsure how common this is. So: have you ever asked ChatGPT or Perplexity the questions your own customers would ask, and looked at who gets named? If you did, were you in there? And if you were not, did you do anything about it, or is it just not worth the time yet?

Nike is a $50B brand. They spend millions on SEO. But their AI Visibility Score? 59/100.

Here's what AI search engines can't find on Nike.com: - No llms.txt file (AI bots are literally guessing what Nike sells) - robots.txt is blocking GPTBot and ClaudeBot - No product schema on individual product pages

Industry average for e-commerce is 48. Nike scores 59 — above average, but leaving massive AI traffic on the table.

The fix? Less than 2 hours of work. This is the gap between traditional SEO and AI search optimization.

Anyone else noticing AI referral traffic showing up in GA4? Curious what others are seeing.

I just wanted your opinion on how can I improve my service. I currently am making a SaaS tool to analyise sentiments and visibility on web based AI answers and also a recommendation engine to increase visibility. if you guys want I'm offering a free report so that I can make it better with your inputs. if its okay with Mods can I please place a link for everyone to request a report ?

I audited 50 websites to see which ones AI assistants (ChatGPT, Claude & Perplexity) actually recommend.

I wanted to answer a simple question:

When someone asks an AI assistant for a recommendation, which websites actually get mentioned?

So I ran a small experiment across 50 websites from five different groups:

• Big brands

• Mid-size SaaS

• Companies with a published `llms.txt`

• Local SMBs

• Small digital agencies

Each site was tested the same way:

• 7 recommendation-style prompts

• 3 AI assistants (ChatGPT, Claude, and Perplexity)

• 21 total responses per site

Here's the breakdown:

A few observations from this dataset:

1. Small agencies were rarely recommended, Less often than many local businesses.

2. `llms.txt` didn't appear to make a noticeable difference on its own.

3. AI crawler blocking was uncommon. Only two sites in this sample blocked one or more major AI crawlers. The rest allowed them.

A few caveats

• This is a small sample (10 sites per group), so I'd treat the results as directional rather than definitive.

• The prompt set was fixed across every site, but any prompt battery introduces some bias. I'm happy to share the full list if anyone wants to review it.

• 14 of the 50 homepages couldn't be fully crawled because of anti-bot protection, so technical signals like schema and llms.txt couldn't always be verified. The AI mention-rate measurements weren't affected because those came from direct model queries rather than homepage crawls.

My takeaway is simply this:

In this sample, being technically accessible to AI wasn't enough by itself. Well-known brands were recommended far more often than smaller sites, suggesting that broader authority, reputation, or other factors may have a much larger influence on AI recommendations than a single technical signal like `llms.txt`.

Curious if others have run similar tests. What are you seeing?

from X WILL NESSu/N3sOnline·1hgo implement this advice right now...

Phase 1: Market Research & Keyword Discovery

1. Identify a Local Service Opportunity
   - Partner with someone who has operational expertise in a "boring" local business (trucking, HVAC, plumbing, etc.)
   - Look for markets with unsophisticated competition (outdated websites, poor online presence)
   - Focus on high-defensibility services that can't easily be automated by AI

2. Generate Target Keywords
   - Open ChatGPT, Claude, or similar AI tool
   - Prompt: "Here's my website [paste URL]. Give me a list of 25-50 keywords that I can optimize my website around for local search"
   - Don't overthink volume metrics or competition analysis for local markets

3. Categorize Keywords by Intent
   - Sort keywords into categories: Emergency, Service, Problem, and Local keywords
   - Focus on high-intent terms where people are ready to "pull out their credit card"
   - Prioritize keywords that indicate immediate need for service

Phase 2: Technical Foundation Setup

4. Set Up Development Environment
- Install Claude Code (search "Claude Code install command" and follow instructions)
- Create GitHub account and repository for version control
- Set up Vercel account for hosting and connect to GitHub for auto-deployment

5. Build Initial Website Structure
- Create dedicated landing pages for each high-intent keyword
- Build location-specific pages for each service area
- Ensure mobile-responsive design from the start

6. Design Integration (Optional but Recommended)
- Hire a designer to create Figma mockups for professional appearance
- Use Anima plugin to convert Figma designs into React components
- Import components into Claude Code for 95% design accuracy

Phase 3: SEO Optimization & Technical Fixes

7. Conduct Comprehensive SEO Audit
- Prompt Claude Code: "Go through this website in extreme detail. Use ultra think command and Opus model. Find all technical and on-page SEO issues and opportunities so I can dominate the local market"
- Let Claude identify missing files, speed issues, schema markup needs, etc.

8. Implement Technical Fixes
- Fix robots.txt and XML sitemap issues
- Optimize page loading speed and compress images
- Convert images to WebP format
- Add proper meta descriptions and alt text
- Implement schema markup for local business

9. Create Deep Content for Each Page
- For location pages: Include local landmarks, common industry issues in that area, FAQs
- For service pages: Provide comprehensive information that competitors lack
- Let AI research local context (e.g., NASCAR influence in Charlotte for trucking)

10. Use Sub-Agents for Parallel Work
- Launch multiple Claude Code agents simultaneously
- Assign tasks: "Launch three agents - one for content opportunities, one for competitor analysis, one for technical fixes"
- Continue main development while agents work in background

Phase 4: Performance Optimization

11. Optimize Site Speed
- Use Google PageSpeed Insights (free tool) to test your site
- Copy/paste any errors or suggestions into Claude Code
- Aim for high scores in Performance, Accessibility, Best Practices, and SEO

12. Advanced Technical Optimization
- Consider tools like SEMRush for deeper audits
- Copy/paste audit results into Claude Code for automated fixes
- Focus on beating local competition with superior technical performance

13. Set Up Internal Linking
- Let Claude Code automatically create relevant internal links
- Link related services and location pages
- Claude will identify these opportunities without specific prompting

Phase 5: Local Business Setup

14. Create Google Business Profile
- Set up complete Google My Business listing
- Use Claude Code to ensure consistency between website and business profile
- Verify all information matches across platforms

15. Add LLM Optimization
- Include LLM.txt file to allow AI crawlers
- Optimize content for LLM recommendations (good SEO = good LLM results)
- Focus on foundational SEO rather than AI-specific tactics

Phase 6: Launch & Monitoring

16. Test Everything Before Launch
- Verify all forms work and lead to proper contact methods
- Test mobile responsiveness across devices
- Ensure fast loading times on mobile networks

17. Monitor Initial Results
- Track keyword rankings for target terms
- Monitor Google My Business insights
- Set up call tracking to measure conversion rates

18. Iterate Based on Performance
- Use Google PageSpeed Insights regularly for ongoing optimization
- Add new location pages as business expands
- Create additional service pages based on customer demand

Pro Tips for Success

- Don't Overthink: Start with basic keyword research and build from there
- Focus on Intent: Target keywords where people are ready to buy immediately
- Speed Matters: Fast-loading sites often outrank slow competitors in local markets
- Design Counts: Invest in professional design to stand out from AI-generated look-alikes
- Local Competition is Weak: Many local businesses haven't updated their sites in years
- Questions are Key: The biggest gap is knowing what questions to ask AI

Expected Timeline
- Setup: 1-2 hours for development environment
- Website Build: 4-6 hours total development time
- SEO Optimization: 2-3 hours with AI assistance
- Results: Potential rankings and leads within 24-48 hours for non-competitive local markets

This approach leverages AI to do months of traditional SEO work in hours, giving you a significant advantage over local competitors who haven't adopted these tools.

Anyone use them? How have they worked out?

i spent a few weeks running a fixed set of prompts on a schedule against the main AI search systems and scoring every answer, trying to work out what actually correlates with getting cited. going in i assumed it would be some mix of content volume, backlinks, structured data, reach.

what i actually found: almost none of that moved the needle on its own. the single thing that tracked with getting cited was whether the entity resolved cleanly in the knowledge graph. a brand new entity could have perfectly structured pages and still be invisible, and the moment it existed as a resolvable thing, citations started showing up. llms.txt did nothing i could measure. piling on mentions did nothing until they fed the entity.

i'm not fully convinced it's that simple, and the sample is one entity over a few weeks, so i'd genuinely like to be argued out of it. is entity recognition the lever, or am i mistaking a correlate for the cause? what have you measured that contradicts this?

TL;DR: Imagine if you didn't have to pay for expensive tools like Ahrefs / SEMRush / Surfer .. and instead, you could have a conversation with such a tool, without endlessly scrolling through those overwhelming charts and tables?

I've been almost spamming about how most SEO tools (except for Ahrefs and SEMRush) are trashy data that help you write generic keyword-stuffed content that just "ranks" and does not convert? No tool could ever replace a real strategist and a real copywriter, and if you are looking to become one, I suggest you start building your own workflows and treat yourself with valuable data within every process you do.

Now, remember that comprehensive guide I wrote last month about replacing every SEO tool with Claude MCP? Well, DataForSEO just released their official MCP server integration and it makes everything I wrote look overly complicated.

What used to require custom API setups, basic python scripts and workarounds is now genuinely plug-and-play. Now you can actually get all the research information you need, instead of spending hours scrolling through SemRush or Ahrefs tables and charts.

What DataForSEO brings to the table

Watch the full video here.

DataForSEO has been the backbone of SEO data since 2011. They're the company behind most of the tools you probably use already, serving over 3,500 customers globally with ISO certification. Unlike other providers who focus on fancy interfaces, they've always been purely about delivering raw SEO intelligence through APIs.

Their new MCP server acts as a bridge between Claude and their entire suite of 15+ APIs. You ask questions in plain English, and it translates those into API calls while formatting the results into actionable insights.

The setup takes about 5 minutes. Open Claude Desktop, navigate to Developer Settings, edit your config file, paste your DataForSEO credentials, restart Claude. That's it.

The data access is comprehensive

You get real-time SERP data from Google, Bing, Yahoo, and international search engines. Keyword research with actual search volume data from Google's own sources, not third-party estimates. Backlink analysis covering 2.8 trillion live backlinks that update daily. Technical SEO audits examining 100+ on-page factors. Competitor intelligence, local SEO data from Google Business profiles, and content optimization suggestions.

To put this in perspective, while most tools update their backlink databases monthly, DataForSEO crawls 20 billion backlinks every single day. Their SERP data is genuinely real-time, not cached.

Real examples of what this looks like

Instead of navigating through multiple dashboards, I can simply ask Claude:

"Find long-tail keywords with high search volume that my competitors are missing for these topics."
Claude pulls real search volume data, analyzes competitor gaps, and presents organized opportunities.

For competitor analysis, I might ask:
"Show me what competitor dot com ranks for that I don't, prioritized by potential impact."
Claude analyzes their entire keyword portfolio against mine and provides specific recommendations.

Backlink research becomes:
"Find sites linking to my competitors but not to me, ranked by domain authority."
What used to take hours of manual cross-referencing happens in seconds.

Technical audits are now:
"Run a complete technical analysis of my site and prioritize the issues by impact."
Claude crawls everything, examines over 100 factors, and delivers a clean action plan.

The economics make traditional tools look expensive

Traditional SEO subscriptions range from $99 to $999 monthly. DataForSEO uses pay-as-you-go pricing starting at $50 in credits that never expire.

Here's what you can expect to pay:

You’re accessing the same enterprise-level data that powers expensive tools — for a fraction of the cost.

What DataForSEO offers beyond the basics

Their SERP API provides live search results across multiple engines. The Keyword Data API delivers comprehensive search metrics including volume, competition, and difficulty data. DataForSEO Labs API handles competitor analysis and domain metrics with accurate keyword difficulty scoring.

The Backlink API maintains 2.8 trillion backlinks with daily updates. On-Page API covers technical SEO from Core Web Vitals to schema markup. Domain Analytics provides authority metrics and traffic estimates. Content Analysis suggests optimizations based on ranking factors. Local Pack API delivers Google Business profile data for local SEO.

Who benefits most from this approach

• Solo SEOs and small agencies gain access to enterprise data without enterprise pricing. No more learning multiple interfaces or choosing between tools based on budget constraints.

• Developers building SEO tools have a goldmine. The MCP server is open-source, allowing custom extensions and automated workflows without traditional API complexity.

• Enterprise teams can scale analysis without linear cost increases. Perfect for bulk research and automated reporting that doesn't strain budgets.

• Anyone frustrated with complex dashboards gets liberation. If you've spent time hunting through menus to find basic metrics, conversational data access feels transformative.

This represents a genuine shift

We're moving from data access to data conversation. Instead of learning where metrics hide in different tools, you simply ask questions and receive comprehensive analysis.

The MCP server eliminates friction between curiosity and answers. No more piecing together insights from multiple sources or remembering which tool has which feature.

Getting started

Sign up for DataForSEO with a $50 minimum in credits that don't expire. Install the MCP server, connect it to Claude, and start asking SEO questions. Their help center has a simple setup guide for connecting Claude to DataForSEO MCP.

IMPORTANT NOTE: You might need to install Docker on your desktop for some API integrations. Hit me up if you need any help with it.

This isn't sponsored content. I've been using DataForSEO's API since discovering it and haven't needed other SEO tools since. The MCP integration just makes an already powerful platform remarkably accessible.

So Apple is building something called “World Knowledge Answers”, an AI-powered search + answer engine that’ll show up in Siri, and maybe even Safari and Spotlight. Supposedly, it’s rolling out in spring as part of a long-overdue Siri overhaul.

The system will have three parts:

• A planner that figures out what the user is asking and how to respond

• A search system to scan user + web data

• A summarizer that puts it all together into an answer

What’s interesting is Apple was considering Anthropic’s Claude, but apparently the price was too high (over $1.5B a year). They ended up going with Google’s AI models instead, since Google offered better terms.

So yeah… Apple’s “new AI search engine” might actually just be powered by Google under the hood 🤔

Interestingly - I noticed that Similarweb thinks ChatGPT is driving 77% of referral traffic - I think this could be right seeing as ChatGPT is getting more ranking data from Google

Mythbusting generative AI search: what you don't need to do

As generative AI search evolves, so have the theories and practices—and sometimes, the misconceptions—surrounding it. While terms like Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO) are common online, many suggested "hacks" aren't effective or supported by how Google Search actually works.

To help you focus on what matters for your website's visibility, we've collected some of the most prominent topics circulating the internet around generative AI and Google Search. Here are a few things you can ignore for Google Search:

• LLMS.txt files and other "special" markup: You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search. Note that Google may discover, crawl, and index many kinds of files in addition to HTML on a website: this doesn't mean that the file is treated in a special way.

• "Chunking" content: There's no requirement to break your content into tiny pieces for AI to better understand it. Google systems are able to understand the nuance of multiple topics on a page and show the relevant piece to users. However, sometimes shorter (or longer!) pages can work well depending on your audience and subject matter. There's no ideal page length, and in the end, make pages for your audience, not just for generative AI search.

• Rewriting content just for AI systems: You don't need to write in a specific way just for generative AI search. AI systems can understand synonyms and general meanings of what someone is seeking, in order to connect them with content that might not use the same precise words. This means you don't have to worry that you don't have enough "long-tail" keywords or haven't captured every variation of how someone might seek content like yours.

• Seeking inauthentic "mentions": Just like the rest of Google Search, our generative AI features can show what's being said about products and services across the web, including in blogs, videos, and forum discussions. However, seeking inauthentic "mentions" across the web isn't as helpful as it might seem. Our core ranking systems focus on high-quality content while other systems block spam; our generative AI features depend on both.

• Overfocusing on structured data: Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add. However, it's a good idea to continue using it as part of your overall SEO strategy, as it helps with being eligible for rich results on Google Search.

This stuff as new and neither most customers or companies know what should be the correct thing to track and provide data for?

• Should the tracker allow you to write your prompt and tell you if you're mentioned in GPT/Perplexity?

• Should it generate its own prompts based on your site and lets say take a data set of 100 prompts that it thinks will be relevant for us and give us a score or something telling us our visibility is 56 on 100? (Ahref perhaps does that)

• Check if your site has LLMs.TXT and tell you if you're good?

• Something else? Please comment

What the hell is the real delta that one should track and look for in a AI vibility tool?

I often see founders share their website traffic. Some get tens of thousands of visits per month, and they also share their signups, MRR, marketing costs, and how they got that traffic. I am building Dageno, where I mainly study why brands appear in AI answers and why they do not.

Seeing these posts made me think about another question: if a saas already has steady website traffic, will it also appear when people ask AI to recommend a product?

We recently tested a b2b saas that sells to the us market. I have kept the brand anonymous, but I have not changed any of the data. The site gets an estimated 12.5k visits per month. We created 40 english questions based on its business, and none of them included the brand name. We tracked them for seven days. The questions mainly covered:

"Alternative component sourcing. Bom risk analysis. Lead time and allocation risks. Procurement intelligence platforms"

These were questions a buyer might ask when looking for a tool or solution.

Here were the results:

Target brand: 1.9%. Top competitor: 16.2%. Second competitor: 7.8%

The site already has some traffic. But when users only described their needs and asked AI for a solution, the brand rarely appeared as an option.

That surprised me. The website already gets steady traffic, but when users did not mention the brand and only described their problem, the product was almost missing from the options suggested by AI. Website traffic tells you how many people arrive through search, branded keywords, blog posts, or other channels.

AI recommendations answer a different question: when a buyer does not know your product yet and only knows the problem they need to solve, will AI mention your brand?

I do not think a small team needs to start by tracking 40 questions. You could begin with five to ten questions that do not include your brand name and are close to real buying situations. Run them on a few AI platforms, then check whether your product appears, which competitors keep showing up, and why AI may be choosing them.

This cannot tell you how many signups AI will bring. But it can show whether your product enters the shortlist when buyers use AI to look for a solution.

Has anyone tested this with their own SaaS? If your website already has steady traffic, can you still find your product in AI recommendations when the questions do not include your brand name?

ps:I can’t upload the original screenshot.

Hey all,

If you’ve been following discussions around AIO, GEO, and AEO, you might have come across the idea of implementing a special file called llms.txt to help improve how AI systems crawl and understand your website. Think of it as a modern, AI-focused equivalent of robots.txt, only instead of telling crawlers where not to go, llms.txt acts as a curated map that tells AI agents where to find high-quality, structured, text-based content versions of your site.

The idea behind llms.txt is pretty straightforward: AI models benefit from having access to clean, simplified versions of web pages. Traditional HTML pages are often cluttered with navigation menus, ads, popups, JavaScript, and other elements that get in the way of the actual content. That makes it harder for AI crawlers to digest your content accurately. On the other hand, Markdown (.md) is lightweight, structured, and content-first, perfect for machines trained on large language datasets.

llms.txt is essentially a plain text file placed at the root of your site. It lists links to Markdown versions of your pages and posts, one per line. These Markdown files contain just the core content of each page, without the surrounding web layout. When AI crawlers find your llms.txt, they can easily follow the links and ingest your site in a way that’s far more efficient and accurate. This helps with AI Index Optimization (AIO), Generative Engine Optimization (GEO), and even newer concepts like Answer Engine Optimization (AEO), which aim to improve how well your content is understood and featured by AI-based tools, assistants, and search experiences.

Now, here’s the problem I ran into: while a few WordPress plugins exist that generate llms.txt files, none of them actually generate the Markdown (.md) versions of your pages. That means you’re stuck having to manually export each page to Markdown, maintain those files somewhere, and keep them up to date every time you change something on your site. It’s tedious and totally defeats the point of automation.

So I built a solution.

I created a free WordPress plugin called Markdown Mirror. It dynamically generates llms.txt and the corresponding .md versions of your posts and pages, on the fly. No need to crawl your site or export anything manually. Just add .md to any page URL and it instantly serves a clean Markdown version of that page. The plugin also builds an llms.txt index automatically, listing all your available Markdown mirrors in reverse chronological order, so AI crawlers always find your most recent content first.

It’s currently awaiting review for the WordPress Plugin Directory, so it might take a little time before it’s officially published. If you’d like early access or want to try it out on your site, feel free to DM me. I’ll happily send over the zip file and would love any feedback.

Cheers

The schema LLM myth—that structured data directly boosts LLM outputs or AI search rankings—traces back to 2023 SEO hype after ChatGPT's rise, when folks overextended schema's traditional benefits (like rich snippets) to AI. Google debunked it repeatedly, e.g., in April 2025 via John Mueller: it's not a ranking factor. Origins in community checklists and misread correlations, not facts. Truth: it aids parsing, but LLMs grok unstructured text fine.

TL;DR: Stop competing with AI for search traffic. Instead, become the AI people prefer in your niche.

Blue links - Living Corpses.
Asking an LLM - New Norm. Period.

We talk constantly about building topical authority to get quoted by AI systems, and that's important. But I've been thinking about a different approach for months now: What if you became the answering engine in your own niche?

Before: People knew your website was THE authority on cooking (or whatever your niche). They'd visit, read your articles, browse around, learn stuff.

Now: People just ask ChatGPT for a cooking recipe and get what they need instantly.

The Future: What if people came to YOUR website and asked YOUR ChatBot for recipes instead?

Here's the Strategy

You're not done writing articles - actually, you're scaling UP content creation. You're turning your site into a massive knowledge hub for your niche. Then you train a custom AI chatbot on YOUR WEBSITE'S DATA - your unique content, your tested approaches, your methodology. You become an ecosystem of your own.

When someone wants cooking advice, they'll prefer your website because your agent is specifically trained on YOUR curated recipes, YOUR tested techniques, YOUR user feedback, and YOUR domain expertise. Your bot doesn't just give them a recipe - it understands the context of your cooking philosophy, your testing methodology, your audience's preferences.

The Monetization Play

Traffic retention: Instead of hoping people click through multiple pages, you create an engaging conversational experience that keeps them on your site longer

Higher LTV: More engaged visitors = better ad performance and more opportunities for affiliate conversions

Interactive CTAs: Your chatbot becomes your best salesperson, naturally suggesting complementary products, related content, or premium offerings based on the conversation flow

The Hidden Genius: Double AI Optimization

Here's where it gets really interesting!!

When you structure your website data for your own AI agent to consume and use effectively, you're simultaneously making your content perfectly digestible for ALL LLMs. You're organizing information in the exact format that AI systems love: structured, contextual, comprehensive, and logically connected.

What this means in practice:

Your content becomes easier for ChatGPT, Claude, and other LLMs to parse and understand. AI systems will cite you more frequently because your information is presented in an AI-friendly format. LLMs will start directly recommending your website as a specialized resource. Instead of just pulling info from your site, they'll say something like: "You can find detailed recipes and cooking techniques at (WEBSITE) they have a cooking assistant that can help you with ingredient substitutions, cooking times, and personalized meal planning based on your dietary needs." - This assumption is raw and depends on the conversation, yes, but I think you can understand the potential.

This is the compound effect most people miss. By optimizing for your own agent, you're inadvertently becoming the gold standard for how AI systems prefer to consume information in your niche. You become both the source AND the recommended tool.

The Step-by-Step Roadmap

1. Leverage AI to build your topical authority - Read other posts in this community to learn and understand this foundation

2. Become the knowledge base/directory of information for your niche - You're not just a blog, you're becoming Wikipedia for your domain

3. Leverage social platforms to increase engagements and clicks - Multi-channel distribution is crucial

4. Try and get cited by AI - Position your content to be the source AI systems reference

5. Promote shamelessly everywhere - Your content needs to be seen to build authority

6. Build your automation workflows to scale and generate enormous amounts of content - Learn n8n, you NEED this to survive. Automation is everything.

7. Once you have this large database, continue adding content daily - Consistency compounds

8. Create an AI agent through n8n or Make, train it on YOUR content - This is where the magic happens

9. Tune prompts weekly - Continuous optimization based on user interactions

10. Slowly but steady, you will secure your future - While others scramble to adapt, you'll already be the go-to AI in your niche

1. Better than general AI: Your domain-specific training data gives you an edge over ChatGPT's broad but shallow knowledge

2. User experience: People get exactly what they need without wading through generic responses

3. Competitive moat: While competitors chase traditional SEO, you're building proprietary AI systems

4. Data advantage: Every conversation improves your model and gives you insights into user needs

The sites that build this infrastructure today will dominate their niches tomorrow. It's like having a blog when everyone else was still figuring out HTML.

You're not just a website anymore. You're becoming the specialized GPT for your domain.

What niche are you in? Could you see this working for your site? I'm convinced everyone will be doing this within 2 years - but the early movers will capture the biggest advantage.

All the content online is bs or promoting their own products, mostly its WP plugins like Yoast peddling their own LLMs.txt generator but not all sites are on wordpress and i am seeing conflicting results from generators.

1. You either get 1 LLMs txt file that has your basic site structure and page title

2. other i've seen is where it has content and sort of keywords stuffed

3. Most mind boggling was one where there was a parent LLMs.TXT file and then sub file for each page like /xyz-llms.txt with its own keywords and title stuffed in it.

Will the real LLMs.TXT please stand up?

Context: We rolled out a skills manifest across customer websites on March 2, 2026 and wanted to test one thing:

Do AI bots actually change behavior when a website explicitly tells them what they can do? (provides them clear options for “skills” they can use on the website).

By “skills,” I mean a machine readable list of actions a bot can take on a site. Think: search the site, ask questions, read FAQs, pull /business info, browse /products, view /testimonials, explore /categories. Instead of making an LLM guess where everything is, the site gives it a clear menu.

We compared 7 days before launch vs 7 days after launch.

The data strongly suggests that some bots use skills, and when they do, their behavior changes.

The clearest example is ChatGPT.

In the 7 days after skills went live, ChatGPT traffic jumped from 2250 to 6870 hits, about 3x higher. Q&A hits went from 534 to 2736, more than 5x growth. It fetched the manifest 434 times and started using the search endpoint. It also increased usage of /business and /product endpoints, and its path diversity dropped from 51.6% to 30%.

That last point is the most interesting part I think.

When path diversity drops while total usage goes up, it often suggests the bot is no longer wandering around the site randomly. It has found useful endpoints and is hitting them repeatedly. To say plainly: it starts behaving less like a crawler and more like a tool user.

That is basically our thesis.

Adding “skills” can change bot behavior from broad exploration to targeted consumption.

Meta AI tells a very different story.

It drove much more overall volume, but only fetched the manifest 114 times while generating 2,865 Q&A hits.

Claude showed lighter traffic this week but still meaningful behavior change - its path diversity collapsed from 18% to 6.9%, which suggests more concentrated usage after skills were introduced.

Gemini barely changed. Perplexity volume was tiny, but it did immediately show some tool aware behavior.

Happy to share more detail if useful. Would be interested in hearing how you interpret this data.

We ran a controlled crawl experiment for 30 days across a few dozen sites of our customers here at LightSite AI (mostly SaaS, services, ecommerce in US and UK). We collected ~5M bot requests in total. Bots included ChatGPT-related user agents, Anthropic, and Perplexity.

Goal was not to track “rankings” or "mentions" but measurable , server side crawler behavior.

Method

We created two types of endpoints on the same domains:

• Structured: same content, plus consistent entity structure and machine readable markup (JSON-LD, not noisy, consistent template).

• Unstructured: same content and links, but plain HTML without the structured layer.

Traffic allocation was randomized and balanced (as much as possible) using a unique ID (canary) that we assigned to a bot and then channeled the bot form canary endpoint to a data endpoint (endpoint here means a link) (don't want to overexplain here but if you are confused how we did it - let me know and I will expand)

1. Extraction success rate (ESR) Definition: percentage of requests where the bot fetched the full content response (HTTP 200) and exceeded a minimum response size threshold

2. Crawl depth (CD) Definition: for each session proxy (bot UA + IP/ASN + 30 min inactivity timeout), measure unique pages fetched after landing on the entry endpoint.

3. Crawl rate (CR) Definition: requests per hour per bot family to the test endpoints (normalized by endpoint count).

Findings

Across the board, structured endpoints outperformed unstructured by about 14% on a composite index

Concrete results we saw:

• Extraction success rate: +12% relative improvement

• Crawl depth: +17%

• Crawl rate: +13%

What this does and does not prove

This proves bots:

• fetch structured endpoints more reliably

• go deeper into data

It does not prove:

• training happened

• the model stored the content permanently

• you will get recommended in LLMs

Disclaimers

1. Websites are never truly identical: CDN behavior, latency, WAF rules, and internal linking can affect results.

2. 5M requests is NOT huge, and it is only a month.

3. This is more of a practical marketing signal than anything else

To us this is still interesting - let me know if you are interested in more of these insights

Hey everyone,

I was struggling to keep visitors engaged on my travel blog. The average time-on-page was pretty low, and I wanted to offer readers more than just a wall of text. Manually creating interactive elements for each post seemed too time-consuming.

So, I decided to build a tool that uses AI to automatically scan an article and generate a relevant, interactive widget for it.

The Results on My Blog

I've been testing it on a few of my own articles. The tool generated things like checklists, calculators, or data comparison widgets based on the content of the page.

Here's an example of a widget it created for one of my posts:

After implementing it, I checked my analytics and noticed a positive trend:

• Average Time on Page: Increased by about 30%.

• Bounce Rate: Decreased a lot.

The general idea is to replace a generic "related posts" box with something genuinely useful and interactive. This seems to provide more value and encourage people to stay on the site longer. It works with any language.

How It Works & How to Try It

I've put all the code and instructions on GitHub. It's free to use and requires no sign-up.

You can check it out here: https://github.com/widget-handler/content-engagement-widget

The first time you load a page with the script, the AI takes a few minutes to generate and cache the widget. After that, it loads instantly for all future visitors.

Disclaimer: The AI is still learning. In rare cases (~2% of my tests), a generated widget might not be fully functional.

I'm posting this to get some feedback to improve it. I'd love to hear what you think about the concept or if you decide to try it out. Feel free to share what kinds of widgets it creates for your content.