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.

I was shopping for a cat water fountain, got overwhelmed by recommendations, and just asked ChatGPT and Perplexity instead.

What surprised me: even when I asked about one specific brand, the AI didn’t only repeat the brand’s own pages. It pulled in Reddit, retailer reviews, YouTube, and review sites too.

So I ran a proper small test.

I used one real brand, PETLIBRO, as a public example and tested 15 pet-water-fountain queries across three buyer stages: problem-aware, solution-aware, and brand-aware. I ran each query once on Perplexity and once on Solution-aware, e.g. “best / quietest cat fountain”ChatGPT 5.5 thinking, then recorded the visible cited sources.

Here’s what stood out:

The brand’s own site did show up, especially in ChatGPT.

But even on brand-aware queries, it was never the whole answer. Reviews, retailer pages, Reddit, YouTube, and third-party tests shaped the answer alongside the official site.

That changed how I think about AEO/GEO.

Optimizing the website still matters: crawlability, product pages, schema, comparison pages, clear claims, etc.

But for branded AI search, that’s only one layer.

I’d also want to know:

- Which review sites does AI repeatedly cite?

- Do retailer reviews show up?

- Does Reddit show up?

- Are there YouTube tests?

- Which caveats does AI repeat?

- Which attributes does AI assign to competitors instead?

- Where in the funnel does the brand disappear?

My takeaway:

A brand’s website makes claims. Third-party sources make those claims believable. AI seems to use both.

So even on your own branded queries, you don’t fully own the answer. AI assembles owned, earned, and community sources together.

Small caveat: this was 15 queries, two engines, one run each, visible citations only, so I’d treat it as an early signal, not a benchmark.

Anyone else tracking AI visibility seeing the same thing? Do your branded-query answers lean on third-party sources as much as your own site?

6/27/2026 update

Small follow-up: I went back and classified the cited domains after a few people here pointed out the “neutral third-party” problem.

The interesting part: “third-party” was not one category.

In this dataset, the sources Perplexity/ChatGPT cited included:

- vet / health authority sources

- Reddit / community threads

- affiliate review media

- retailer pages

- competitor brand pages

- seller-owned advice blogs

- manufacturer / supplier content

- YouTube videos

- app-store/review signals

So the sharper takeaway for me is:

Third-party does not mean independent.

A brand page has one incentive. But a review roundup, retailer page, competitor blog, manufacturer guide, YouTube video, and Reddit thread all have different incentives too.

I also checked the “advice-style” sources specifically — the ones that look like neutral reviews, comparisons, or guides rather than obvious stores / Reddit / vet pages. Out of 16 advice-style sources, only one had no visible product-commerce incentive I could verify. The rest were affiliate-disclosed, seller-owned, manufacturer-owned, site-level affiliate, or unverifiable/page-changed.

That doesn’t mean those sources are bad or useless. But it does mean AI product answers are not built on a neutral web. They’re built on an incentive map.

This also made me think the audit question shouldn’t just be “which sources does AI cite?” but “what does each cited source want?”

How do you track if ai chats recommend your products? Seems like chatgpt's approach to suggesting products is still changing. Has anyone managed to properly track it?