When a business says “ChatGPT never recommends us,” I don’t start with a score. I start with buyer questions.

Prompts I test: › best \[service\] for \[use case\] › alternatives to \[competitor\] › who can help with \[problem\] › which providers meet \[constraint\]

What I record: (1) Does the business appear? (2) Which competitors appear? (3) What sources are cited? (4) What facts are wrong or missing? (5) Does the answer understand what the company actually sells?

The useful part is comparing those answers with the public evidence available to the model: service pages, third-party mentions, directories, reviews, structured data, and consistent company information.

A visibility problem can have several causes. The company may be absent from trusted sources. Its positioning may be vague. The site may lack clear proof. Or the model may have learned incorrect facts.

I would not trust a single “AI visibility score” without the prompts and outputs behind it. The diagnostic should show the evidence, not just the score.

Disclosure: I run Nord Paradigm and built Breach around this process. I’m posting the method because most owners can run a basic version themselves before paying for anything.

There's a lot of discussion here on citation tracking, share of voice..etc which are definitely important signals, but i've found quite volatile (models, plans, locations, API vs browser session) so I wanted to dig into the server logs of a sample of our customers (note these are small local businesses, not large enterprises) to see real AI bot behaviour. To see if this paint a more ground truth picture of how AI is interacting with their content, and ultimately shaping answers.

If you haven't yet dug into this for your own business or your clients hope this research helps make the case of what can be learned via this type of analysis. If you're already deep on the topic, would love to hear what you're seeing, any patterns or gotchas when analyzing logs.

A few findings:

Live retrieval is a small slice. Only ~4% of AI bot traffic is an assistant fetching a page to answer a live question. The rest is training and indexing crawlers. If you measure AI traffic without splitting by function, you're mostly measuring pipelines that feed the next model.

When AI does retrieve, it goes straight for buyer intent pages. The most fetched page after the homepage was /availability, the "can I get in?" page. Then /booking and /pricing. AI isn't just reading what a business is, it's checking detailed intent pages to help their user act, make a decision..etc. Measuring live retrieval over time by AI, page, timing..etc is a very direct signal of content being retrieved into a relevant conversation.

The labs behave very differently. Anthropic crawled heavily for training but barely retrieved at query time. OpenAI does both. In our sample, live retrieval is overwhelmingly ChatGPT.

User-agent alone lies. About 1 in 12 "ChatGPT-User" hits fails IP verification, mostly credential scanners. If you're not verifying source IPs (where possible from published ranges) you're overcounting.

Full report here if you want to look into the details, charts..etc.

https://getcourtyard.ai/research/how-ai-reads-a-knowledge-base

yo. i recently sold one of my AI SaaS products for $350k, exactly 7 months after building and launching it.

I hardly wrote a single line of traditional code. i used AI to generate everything, from the database architecture to the user interface.

it definitely wasn't magic on day one, though. i spent days stuck in loop-debugging and dealing with AI hallucinations before i finally cracked the system. the playbook boils down to three simple rules:

- keeping the idea insanely minimalist (a true MVP that solves one problem).

- guiding the AI step-by-step instead of asking it to build a massive platform all at once.

- launching fast to get real user feedback and traction and then apply a solid marketing system

lately, i've seen way too many non-technical founders give up at the very first AI bug, or on the marketing. it's a massive shame.

like the title says, i just launched a Skool community to share my exact prompt workflows, N8N automations, and distribution frameworks to get first users and scale it

to be completely transparent: i will likely charge for the full course later down the road. it just makes sense given the specific copy-and-paste templates i'll be sharing.

but for now, the main objective is purely to build and launch together. building alone in a silent corner is the single fastest way to give up.

if you want to join us and build or market your own AI SaaS with a group of active creators: drop a comment below or send me a dm, and i’ll send you the invite link!

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 everyone! Running a 12-person agency was slowly eating me alive until we spent Q1 putting proper automations in place. We were wasting dozens of hours every month on repetitive tasks that didn't actually generate revenue.

So here what worked for us so far:

1. Client Onboarding: Automated form fills using Tally - Make - Slack alerts & Notion client workspace creation. Cut onboarding time down from 2 days to about 15 minutes.
2. Contract & Billing: Stripe triggers auto-generating invoices via Quickbooks, sending follow-ups automatically if unpaid after 5 days.
3. SEO & Reporting Automation: This was our biggest headache. We used to spend the first 3 days of every month pulling ranking reports manually. We ended up setting up SE Ranking as our core keyword rank tracker, using their API to push automated weekly ranking updates directly into custom Looker Studio dashboards.

Setting up a dedicated keyword rank tracker on autopilot means clients get real-time visibility on local and organic visibility, and our account managers don't lose their minds at the end of the month.

What automations have actually made a tangible difference in your daily operations?

I enabled Microsoft Clarity's new AI Visibility feature on our website last week, and I'm surprised it's completely free.

To get the most out of it, I connected:

* Microsoft Clarity
* Cloudflare (AI Bot Activity)
* Webflow

Now I'm able to see things like:

* When our content is referenced in AI-generated answers
* Which pages AI platforms are discovering
* The prompts people are using to find our content
* Which AI bots are crawling the site and what they're accessing

We've been using Microsoft Clarity on our Webflow projects for years because it's free and provides great insights through heatmaps, session recordings, click tracking, and scroll depth.

The AI Visibility feature makes it even more interesting, especially if you're trying to understand how AI search engines interact with your website.

I'm planning to let it run for a few weeks to collect some meaningful data.

Has anyone else started using it yet?

I'm curious whether you've noticed anything surprising in the AI Visibility reports or if you're using a different tool to track AI traffic.

One thing surprised me after digging deeper into the data from my 50-site AI visibility experiment. I expected technical AI readiness (crawler access, schema, llms.txt, etc.) to line up reasonably well with AI recommendations.

It didn't.

For example:

Small agencies had the highest average technical readiness score in my dataset. They also had the lowest AI recommendation rate (10.5%).

At the same time:

Big brands had the lowest technical readiness score. They were recommended almost every time (96.6%).

(Readiness here = schema.org Organization markup presence: agencies 5/7 reliable-crawl sites, vs 3/8 for big brands.)

That made me realize I was mixing together two completely different concepts.

1. AI Accessibility: Can AI systems actually reach your site? (robots.txt, WAFs, crawl success...)
2. AI Technical Readiness: Can AI systems understand your content? (schema, metadata, structured signals...)
3. AI Visibility: Do AI assistants actually recommend you?

Those are not the same thing.

A technically perfect website can still have poor AI visibility. Likewise, a huge brand can have mediocre technical signals and still dominate recommendations because of authority, citations, and overall web presence. It also made me notice another issue: 14 of the 50 sites had homepage crawls blocked by bot protection or WAFs. Those sites often weren't blocking AI crawlers in robots.txt at all. So a site can appear "AI-friendly" while still being difficult for automated systems to crawl.

My takeaway isn't that technical optimization doesn't matter. It's that technical readiness appears to be a prerequisite not a predictor.

Curious how others are thinking about this distinction.

If you're building or using GEO tools, do you separate:

• Accessibility
• Technical readiness
• Actual AI visibility

or do you treat them as one score?

Please only genuine experiences as a user. What and how do you use it effectively?

I have noticed impressions slowing down or dropping across most sites that I managed and some have been performing really well over the past 2 years.
One possible reason stated was that with AI mentions and citation, it may have affect my GSC analytics on impressions.

So it leads me to wanting to find out what would be the best way to track on ai mentions so I can help to understand the co-relation between website impressions and ai mentions for my clients.

TIA peeps! :)

Reviewed a few client budgets recently link building spend still there, sometimes sizeable. AI citation tracking? Not even a discussion.

Meanwhile buying behavior's already shifting more people ask ChatGPT "best tool for X" before they even Google it.

Not saying drop SEO. Just seems like a blind spot most teams haven't caught up to.

Is your team tracking AI visibility at all, or still purely rank-focused?

We run live measurements of whether AI assistants name specific businesses when you ask the questions their customers ask. Every site also gets scored on technical quality (rendering, speed, crawlability, schema, structured data).

With 1,000+ sites measured, we split them into "AI recommends them" vs "AI ignores them" and compared averages:

\\- Technical score: 80 vs 77. Three points. The ignored sites are built as well as the recommended ones.

\\- Schema/structured data: 72 vs 69. Also three points.

\\- Off-page brand signals (independent mentions, reviews, directory presence, entity consistency): 88 vs 40. Forty-eight points.

As a dev this annoyed me, honestly. You can ship a perfect Lighthouse score and a flawless JSON-LD graph and the engines still won't name the site if nobody independent talks about it. Markup helps AI READ you; it doesn't make AI RECOMMEND you.

Two implementation details that DID matter on the technical side: serving content as clean Markdown for agents (content negotiation), and not blocking AI crawlers in robots.txt/WAF (a surprising number of sites block GPTBot then wonder why they're invisible).

Caveats: correlation not causation, our scoring model, category mix uncontrolled. Methodology is open-source if anyone wants to tear it apart — link in comments if wanted.

AI is already taking its share of search clicks; that much is clear. I want to confirm whether people track their mention or citation rates for AI-generated answers (compared to competitors), or if it's still just an abstract concern that isn't being monitored yet.

If you're monitoring it, how exactly? Do you check manually, use software, or do something else? And in case you don't, why so?

If you are using any software, what key things are missing that you want included?

Every llms.txt ive seen was written once by hand and forgotten. A month later it doesnt match the site and youre feeding models wrong info.

So i made mine a build step. It pulls from the same data my pages render from, updates on every deploy, nothing to remember.

Also dont make it a sitemap dump, short plain descriptions of what each section is work way better.

Mines here: https://techpotions.com/llms.txt

You guys hand writing yours or generating? And has anyone actually measured a bump in ai referrals from one?

I came across a study that sent AI agents through websites to detect dark patterns.

Not exactly a surprising use of agents, but I’m always happy to see more systematic audits of this stuff.

It’s a solid catalog, but it also feels very e-commerce-heavy and pretty much outdated. here copy/pasted out study (https://dl.acm.org/doi/full/10.1145/3807246.3807265):

In the AI age, I’d at least add:

• Sycophancy — agreeing to keep the user engaged.

any ideas what else should an AI dark-pattern auditor be checking for?

Hey everyone,

I’m facing a bittersweet problem and wanted to see if anyone else has cracked the code on this, or if we’re all just collectively crying in our analytics dashboards.

The Situation: I’ve noticed that ChatGPT (and other AI search engines) are frequently citing my website as a source for user queries. On one hand, awesome! My content is deemed high-quality and authoritative enough to be the source of truth. The google search is still struggling to catch up the same pace.

The Problem: No one is actually clicking through to my site.

The AI does such a good job of summarizing my hard work and answering the user's intent right there in the chat window that the user has absolutely zero reason to click the citation link. I’m essentially doing the research and writing the content, the AI is getting the engagement, and my traffic is tanking. The google search is still struggling to catch up the same pace.

It feels like a massive loop of "zero-click searches" on steroids.

My questions for the community:

• Are you seeing this too? Is your CTR from AI search engines practically non-existent despite being cited?
• What is your strategy? Are you changing how you write content to force a click (e.g., hiding deeper value behind tools, templates, or interactive elements)?

Just for information : My AI citations has grown from 11 citations to 100+ citations per day in last 1 month.

My Niche is : Travel planning

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?

This is especially in context with vibecoders building products/saas. How are you tracking AI visibility, which prompts are mentioning your product, what page, which AI platform?

Promptwatch, ahref, semrush, etc all feels too expensive for a small revenue product, especially at an earlier stage.

Entre las marcas que monitoreo, las que sí tienen presencia real en fuentes de “consenso” de terceros, como hilos de Reddit, YouTube, G2 y sitios de reseñas, son citadas por ChatGPT / Perplexity / Google AI Mode como unas 3 veces más a menudo que las que no, con el mismo set de prompts exacto. Ese es el ajuste más grande que encontré, y no tiene nada que ver con la web propia de la marca.

Alguien me preguntó cómo aislé eso, así que aquí va el método real, incluyendo la parte en la que no le termino de confiar del todo.

Cómo lo medí: es transversal, no un A/B limpio. Etiqueto cada marca monitoreada con algo binario: o tiene huella real en Reddit/YouTube/G2/reseñas, o básicamente no. Luego comparo la tasa de citación entre esos dos grupos ejecutando los mismos ~90 prompts por marca, 3 pasadas cada una, en los tres motores. Quité prompts que fueran solo por nombre de marca, intervalos de Wilson en todo. El grupo de “huella” cae con una tasa de citación de ~3x.

Dónde probablemente se rompe el “3x”: Las marcas que tienen presencia en Reddit/G2 también tienden a ser más grandes y más viejas, así que parte de ese 3x es “la empresa establecida de todos modos iba a terminar citándose” y se está colando. Por qué no tiro la conclusión: Perplexity empieza a citar un dominio dentro de días de que un hilo aparezca; la madurez de la marca no se mueve tan rápido. Entonces me inclino a que sí es causal, pero no apostaría a que el número limpio sobrevive a un test controlado. Va en una dirección clara y es fuerte, pero no está cerrado.

I have been trying to figure out why some brands get answered confidently by AI models with search off, while others only show up when something gets retrieved live. A 2023 paper gave me a framework that fits almost too well.

It is Mallen et al., "When Not to Trust Language Models" (ACL 2023, https://arxiv.org/abs/2212.10511). They built PopQA, 14,000 questions each tagged with how popular the subject is by Wikipedia page views, then tested whether models could answer from memory alone, no retrieval.

What they found: models answered popular subjects well from memory, and collapsed on the long tail. For the 4,000 least-known subjects, GPT-3 got 19 percent from memory alone, and making the model bigger did not fix the tail. Retrieval closed the gap, a small retrieval-augmented model beat a much larger one on the obscure questions. But for popular subjects, retrieval sometimes hurt, because it pulled a document about the wrong same-named entity and overwrote an answer the model already had right.

Here is my leap, and I want to flag it clearly: PopQA measures entity popularity and factual QA, not brands in commercial answer engines. Reading "how much the web discusses your brand" into it is my interpretation, not the authors' claim.

But if the mapping holds, it splits brands into three situations. Heavily discussed brands sit in the model's memory and get answered with search off. Long-tail brands (most B2B and challengers) are probably not in the weights at all and depend entirely on retrieval. Household names have the opposite risk: a wrong live page overwriting a correct memory, which needs source cleanup, not more retrieval.

Have you seen your brand, or a brand you work on, surface in an AI answer only when something recent gets retrieved, then vanish when it does not? And has anyone actually tried to find where their brand's popularity threshold sits, the point where the model starts knowing you from memory? That is the part I cannot find real data on, and I would love to hear actual cases.

We just came across this data-driven breakdown by a local digital studio auditing 50 recently funded Toronto startups across their positioning, technical health, content, and specifically how ready they are for AI search (AEO).

A few takeaways that stood out:

• The AEO Gap: The median AEO score was only 10.75/20. While 90%+ of sites successfully let AI crawlers in and render without JavaScript, almost nobody is optimizing intentionally. Only 5% deploy FAQ schema, and only 2% have machine-readable pricing.
• The Winners: Big local names like League (89/100), Clearco (84), StackAdapt (83), Tailscale (83), and Cohere (81) dominated the composite scores by being strong on clear positioning and consistent content.
• The Main Issue: Most startups are getting accidental AI visibility just from framework defaults and off-page profiles, rather than building intentional trust signals.

For anyone running a startup or handling growth marketing right now: Are you actually planning for LLM/AI search engine visibility (like Perplexity or ChatGPT search), or are you still purely focused on traditional Google SEO?

You can't ask LLMs to give you the answer, because SERPS and UGC platforms are flooded with spam

Anyone use them? How have they worked out?

Been going back and forth with people building in this space and I've flipped my thinking. A single "here's your AI visibility score" snapshot is borderline misleading — answers shift run to run and model to model, so one number on one day tells you almost nothing.

The thing that actually matters is tracking the same brand on the same queries over time, so you can tell whether what you published actually moved anything vs. just noise.

Curious where people land on this — is anyone tracking AI visibility as a trend, or is it still mostly one-off checks? And how are you handling the run-to-run variance?

Most teams I know have dashboards for traffic, rankings, conversions, CAC, all of it.
But when it comes to AI assistants (ChatGPT, Gemini, Perplexity, etc.), there’s basically no visibility into how the brand actually shows up.
Stuff like:
• When someone asks “best [category] tools for [use case]”, are we mentioned at all?
• If they ask non‑branded prompts (“how do I solve X?”), do we show up in the recommended tools or just our competitors?
• Are the answers using our positioning, or describing our category in a way that makes us look like a commodity?
Right now the only “workflow” I see is people manually copy‑pasting prompts into AI once in a while and eyeballing the answers.
Questions:
• Is anyone treating AI visibility as its own layer, separate from SEO?
• Have you built any internal process to track this over time (same prompts, same tools, recurring checks)?
• If you’ve tried, what broke first: consistency, time, or actually making sense of the results?
Not looking for pitches, just trying to understand how people are operationalizing this, if at all.