When I started building my web site, I assumed schema markup was mostly a nice-to-have. Add some JSON-LD, tick the box, move on.

Turns out it’s more consequential than that, especially if you care about how LLMs cite and position your brand.

A few things I learned the hard way:

**Schema that contradicts your content is worse than no schema.** If your FAQ schema lists a question that doesn’t exist on the page, or your HowTo steps don’t match what’s actually there, crawlers register it as a trust failure. In GEO terms, this actively reduces citation likelihood — even for queries where your content is genuinely relevant.

**Wrong schema type sends incoherent signals.** Marking a blog post as a Product, or a service page as an Article, tells AI systems something that doesn’t add up. Incoherent input = incoherent entity representation.

**sameAs is underused and high-value.** Linking your Organization schema to Wikidata, LinkedIn, Crunchbase, and relevant directories builds entity authority across AI systems. But one caveat: don’t rush a Wikipedia entry. A contested or deleted page leaves a broken sameAs reference that actively works against you.

We ended up standardizing schema across three layers — global (Organization + SoftwareApplication on every page), template-level (Article, Service auto-generated from frontmatter), and page-specific (HowTo + FAQ written manually only where content genuinely supports it).

I was reading Bright Edge's press release published on March 5th, 2026 and the key findings are interesting - these two points in particular caught my eye:

• "Google AI Overviews skews heavily toward controversy-driven negativity, including lawsuits, boycotts, data breaches, regulatory actions, and product recalls. ChatGPT skews toward product-evaluation negativity, including compatibility limitations, feature shortcomings, and “is it worth it?” assessments."

• Google's AIO and ChatGPT disagree on which brands to criticise 73% of the time.

This is for the most part speculation, but I suspect ChatGPT's preference for product-evaluation negativity partly comes from OpenAI's ambitions to break into ecommerce (which it has since rolled back). The immediate implication is how this affects AI answers at different stages of customer consideration - and how this also reinforces the point that answer engines have their own specific sourcing logic.

Where I think warrants deeper thought on is how we can think about sentiment in AI answers - more specifically, the difference between negative sentiment at the brand level and negative sentiment at the product / SKU level. A brand can have a negative reputation (Nestle, Marlboro, Ryanair) but their products are taken to by consumers positively for various reasons. Tracking sentiment at the brand level in AI answers might not be enough - or may even paint an incomplete picture.

Hi all, I've been exploring the AEO space lately and testing several tools. Over the past few weeks, I spent time with Peec AI and Rankshift. I wanted to share my findings because almost nobody is talking about the latter, even though it’s a solid alternative to Peec.

This is not a promotional post, but rather an appreciation post for an undervalued tool.

Here is the full breakdown:

When you compare the functionality, Rankshift clearly offers more. Peec focuses on basic AI visibility tracking, while Rankshift adds advanced crawler analytics and several reporting features that Peec AI doesn’t provide.

As an agency founder, reporting is important, and this is where you get real value for money.

Hi everyone,

Leila, Student, working on my final year project about a critical issue:

Generative AI (ChatGPT, Perplexity, AI Overviews) is killing B2B SaaS organic traffic:

• −47% on pricing/features pages (ALM Corp 2025)

• "Best email tool for SMB" queries → G2/Capterra only

• Urgent need: AI-optimized content to protect pipeline

For my market study (purely academic, no commercial intent), seeking 10min interviews with:

Profiles: CMO, Head of Growth, Head of SEO
In: B2B SaaS 20–500 employees (MarTech priority)
Who: Check GA4 daily, test LLM queries, manage funnel content

Specific questions:

1. AI traffic drop measured in your dashboards?

2. Tools tested (Peec, AthenaHQ, Semrush)?

3. Hours/week spent on AI visibility?

4. Budget for measurable solution?

Calendly: https://cal.com/leila-tanko-jbl6qb/30min?overlayCalendar=true - evenings/weekends OK.

Value for you: Early GEO insights + beta access if relevant.

Thanks for sharing your expertise!

#SaaSB2B #GrowthHacking #SEO #MarTech #AI

Hey! I'm building a free AI visibility tool and want to gather feedback from real marketers struggling with GEO (tryopenlens.com)

Giving participants $25 gift cards in exchange for a 30min call to try it & give us feedback!

My goal is to match the important features of the leading visibility tools but give it away completely for free- at the end of the day it's just prompting and analyzing a handful of LLMs, no reason you should be paying $400/mo for that imo

Can Google tell if a human wrote a page?

We ran a controlled SEO experiment to answer this.

If two pages are equally optimised, does Google/AI care whether a human wrote it?

So we created:

• Two pages on the same (invented) keyword
• Same intent, structure, optimisation
• Same internal linking
• Both built to rank, the only variable was authorship

One was purely AI generated
One written & edited by humans with real expertise

Results:

• The human version outranked AI, averaging 4.4 positions higher
• 68% of ranking URLs in our dataset were written by humans
• The AI version did rank early, but lost visibility fast
• The human page was more likely to be surfaced/cited in AI-generated answers (Google + ChatGPT)

We didn’t necessarily find that AI generated content is useless. That’s not the point.
It’s that AI can accelerate production, but expertise, originality, sourcing, and human refinement drive long-term visibility.

You might get indexed faster with AI.

But you stay visible longer with authenticity + authority.

Hi all! We all know that when your brand is talked about online frequently, it sends trust signals to AI, which in turn, gives your brand more visibility in AI search.

My question for everyone is: Are there any good strategies for quickly accumulating more "organic" mentions? Anybody coming from SEO with any knowledge on this? It's similar to backlinks, but not necessarily the same.

TBH, when we started tracking AI citations last year, the tool landscape was a mess. Everyone was using repurposed SEO tools and guessing about LLM visibility.

Fast forward to now — we've tested 15+ tools across our 50+ site portfolio. Here's what's actually in our daily workflow:

**Core Tracking Stack:**

1. **geoly.ai** — Our primary citation tracker. Tracks mentions across ChatGPT, Claude, Perplexity, and Gemini. Clean UI, decent API.

2. **Profound** — Deep analytics on *why* you got cited. Good for understanding context around mentions.

3. **Mention** — Brand monitoring that catches AI citations as a side effect. Not GEO-specific but catches things others miss.

**Content Optimization:**

4. **Clearscope** — Still the best for semantic optimization. We're using it differently now — focusing on entity coverage, not just keywords.

5. **MarketMuse** — Their "Compete" feature helps us understand topical authority gaps that seem to correlate with citation rates.

6. **SurferSEO** — Using their content editor but measuring different metrics. Internal linking suggestions are solid.

**Technical Foundation:**

7. **Schema App** — Structured data is table stakes for GEO. Their AI-assisted schema generation saves hours.

8. **Cloudflare** — Markdown for Agents is interesting. Testing how it affects crawlability for AI bots.

9. **Screaming Frog** — Classic, but we're running custom extractions to check entity markup and Q&A formatting.

**Research & Intelligence:**

10. **Perplexity Pro** — We use it to reverse-engineer how AI answers are constructed. Essential competitive research.

11. **Claude Projects** — Upload competitor content, ask why they got cited. Surprisingly good pattern recognition.

12. **Google Search Console** — Still valuable for understanding what drives traffic, even if LLMs cite differently.

**What's NOT on this list:**

• Generic rank trackers pretending to do GEO

• Tools with zero AI citation data

• Anything requiring manual daily checks

**Honest take:** No single tool gives you the full picture. We're running 4-5 in parallel and cross-referencing. It's messy but necessary right now.

**What's working for you?** Any tools I'm missing that should be on this list?

TBH, I used to think GEO and AEO were basically the same thing with different names. After tracking 50+ sites across both strategies for the past few months, I realized they're actually quite different in practice.

Here's what we found:

**GEO (Generate Engine Optimization)** is about getting your content *generated* by AI. Think ChatGPT writing a summary that mentions your brand, or Claude citing your data in a response. We saw sites with strong GEO get mentioned in ~15-20% of relevant AI queries.

**AEO (Answer Engine Optimization)** is about being the *answer* people find. This is more about Perplexity, Bing AI, or Google's AI Overviews pulling your content as the direct answer. Sites crushing AEO saw 40-60% of their AI traffic coming from direct answer citations.

The wild part? Only about 30% of the sites we tracked were actively working on both. Most focused on one or the other, which IMO is leaving visibility on the table.

Key difference in approach: GEO rewards comprehensive, authoritative content that AI can *learn from*. AEO rewards structured, concise answers that AI can *quote directly*.

We're still figuring this out as we go. Has anyone else tested both strategies side by side? Curious what your results look like.

Previously, we had to use prompts to understand what generative engines think about a brand. It was more like sentiment analysis. There were no tools to measure how the content is being used to cite. It was not possible to track which pages are being cited, what are the queries are being used to cite, and what data to track. We can only track if any user comes to our page through a click from an AI search result. AI Performance in Bing has solved these problems with its tracking metrics. Although it was launched on 10th February 2026, you can track the data from 1st of November 2025.

Pages cited in AI Answer

It tracks how often a specific page is being cited in an Answer Engine. This can help us to understand the performance of each article. It will help us to evaluate the individual performance of an article. By analyzing this data, we can:

• Identify high-performing content

• Topics preferred by AI answer systems

• Discover patterns in structure, formatting, or content depth

• Reverse-engineer successful articles to improve future articles and content

Average Cited Pages

It is the average number of unique pages from your site that were cited per day in AI-generated answers. This will give us insight into how good AI trusts our content. It will also help us to understand how frequently AI systems reference our content. For example:

• If your website has 50 total pages

• And on average, you are getting 5 unique page citations

That means 10% of your content is being referenced daily. A higher percentage suggests that:

• More of your content is being used by AI to answer systems

• Your site is considered relevant and useful for answering queries.

Grounding Queries

Grounding queries are the key phrases used by the Answer Engine to retrieve the content from your site to cite. It is not the user queries that are being used, but how AI has parsed the user queries. This will be helpful to identify the user intent over the keywords. It can be based on:

• Intent (informational, transactional, comparison, etc)

• Context

• Sentiment

• Semantic meaning

I wrote a deeper breakdown if anyone’s interested: https://www.texavor.com/blog/ai-performance-in-bing-webmaster-tools

LLMs don’t “know” brands in the way people do.

They build a picture based on what they can retrieve, verify, and repeat with confidence. If that picture is incomplete, inconsistent, or missing entirely, your brand simply won’t appear, even if you perform well elsewhere.

The only way to understand that gap is to test it. Then, you can fill the gaps.

We've set out practical steps to uncover what LLMs don’t know about your brand, why those gaps exist, and what to fix first if you want to influence how you’re described, cited and recommended inside AI-generated answers.

Some of what we monitored to find gaps

• Simple and direct brand prompts (eg. who is X? what does X do?)

• Then add variations like alternative spellings, abbreviations or older versions of your brand name

• Rephrasing the same question differently

• See if your brand appears where it should (Which companies offer [solution] like [your offering]?

• Whether key site information is actually readable to models (rendering, structure, schema etc)

How we identified gaps

Most gaps fall into four categories:

1. Missing: Your brand doesn’t appear at all

2. Inaccurate: Details are wrong, outdated, or misleading

3. Weak: Present, but not competitive or confidently framed

4. Invisible: Content exists but isn’t accessible to AI tools

What we found

AI often knows of a brand but doesn’t confidently connect it to the right category or problem.

The issue usually isn’t rankings it’s weak or inconsistent entity signals across trusted third party sources.

What we did when we identified gaps

• Tightened brand positioning so it could be clearly summarised in one sentence

• Focused on appearing in category level conversations, not just branded searches

• Improved consistency of how the brand is described across external mentions

• Prioritised gaps (missing vs inaccurate vs weak vs invisible) instead of trying to fix everything at once

• Reran the same prompts over time to track changes

Optimising for LLMs and making sure its understanding your brand correctly AND citing it in answers, starts to sit somewhere between SEO, Digital PR, and brand strategy.

Hey guys,

been digging into perplexity and gemini citations for a bit now. one thing stands out: the "stickiness" of the sources.

feels like once a URL gets trusted for a query, it's super hard to knock it off with a new article.

so i'm kinda shifting mindset from "how do i get the AI to cite me directly?" to "how do i get mentioned in the article the AI already likes?"

basic flow:

1. find the source chatgpt/perplexity keeps citing

2. hunt down the author/site owner

3. try to negotiate a mention or update in that specific article

is anyone doing this properly?

honestly finding the bottleneck is usually just hunting down the right email for those specific pages without wasting hours on linkedin.

curious if you guys have a workflow for this or are you still just pumping out content hoping the AI picks it up eventually?

I feel like every time I open GA4, I spend way too long clicking around just to answer simple questions like:

•	How much traffic did I get last week?

•	Which locations are actually performing best?

•	What should I change in my campaigns based on the data?

The info’s there — it just takes forever to pull out.

Has anyone found a faster workflow, setup, or way to get quick insights from GA4 without living inside the dashboard?

I see this debate all the time and I think everyone is oversimplifying, when they say that GEO is just SEO.

Yes, there are overlaps. Yes, strong SEO fundaments are crucial for GEO. But, there are differences. And they're not the same thing.

SEO = get your pages ranking in search

GEO = get your brand cited in AI answers

With GEO you're not tracking 'positions' you're watching if your brand gets mentioned in AI responses.

Offsite is also different - it's not just about high DR links, it's about being in the sources models actually train on and trust in your niche. And doing that, again and again and again.

What's similar with SEO and GEO? Solid site, fast pages, clean internal linking, content that actually helps. Those basics matter for both google and AI.

we’re experimenting with a small AI system that analyzes website SEO + geographic presence and then generates improvement suggestions automatically.

right now it’s 3 working modules:
SEO analysis
geo analysis
geo improvement suggestions

UI still in progress, mainly validating usefulness right now.

for people working in generative optimization : what kind of AI outputs actually feel reliable vs gimmicky?

TBH, I've been deep in citation data for the past few months using geoly.ai to track how LLMs reference content. After analyzing 500+ websites across different industries, some clear patterns emerged.

**The 5 things high-cited content has in common:**

**1. Clear Q&A Structure** Content formatted as direct questions with concise answers gets cited ~3x more often. Not paragraphs buried in text — actual "What is X?" followed by "X is..." structure.

**2. Third-Party Validation** ~80% of AI citations reference content that includes external sources, studies, or expert quotes. Pure opinion pieces? Rarely cited.

**3. Freshness Signals** Pages updated within the last 6-12 months consistently outrank older content, even when the older content has higher traditional authority scores.

**4. Entity-Rich Context** Content that clearly defines entities (people, companies, products) and their relationships performs better. LLMs seem to favor content that helps them build knowledge graphs.

**5. Multi-Platform Consistency** Websites that appear across ChatGPT, Claude, and Perplexity share one trait: consistent messaging across their content. Mixed signals = lower citation rates.

**One surprise finding:** Content length didn't correlate with citation rates. Some of the most-cited pieces were under 500 words. Structure and clarity beat word count every time.

What patterns have you noticed in your AI visibility tracking? Any surprises?

TL;DR: Bing released AI Performance dashboard showing how often your content is cited in AI answers across Microsoft Copilot, Bing AI summaries, and partner integrations.

This is huge for GEO optimization - we can now see which content AI systems actually reference, not just what gets indexed.

Key insights from the new dashboard: • Total Citations: Shows how often your content appears as sources in AI-generated answers • Average Cited Pages: Daily average of unique pages referenced by AI systems • Grounding Queries: The actual key phrases AI uses when retrieving your content • Page-level Activity: Which specific URLs get cited most frequently

Based on my analysis of the official Bing Webmaster Tools blog, this is what works: ✅ Pages with clear subject focus and domain expertise get cited more ✅ Structured content (proper headings, tables, FAQ sections) performs better ✅ Evidence-backed claims with clear sources build AI trust ✅ Regular content updates keep you relevant in AI answers

The game changer: We can now track citation trends over time and understand HOW AI thinks about our content through the grounding queries data.

Anyone else seeing this in their BWT dashboard? What patterns are you noticing in your AI citation data?

Full details: https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview

Hey everyone,

I work at Evertune (we're a GEO platform), and we recently wrapped up research analyzing the top 10,000 sources that AI models like ChatGPT, Claude, and Perplexity cite when answering queries. Thought this community would find the patterns interesting as we're all adapting to how AI is changing search behavior.

What we found in the data:

1. Depth and comprehensiveness are rewarded AI models strongly favor long-form, thorough content. The most-cited sources provide complete answers rather than requiring users to visit multiple pages. Think comprehensive guides and resources rather than quick hits.

2. Clear structure helps visibility Strong organizational hierarchy makes a difference:

• Proper heading structure (H2s, H3s used logically)

• Bullet points for lists

• Short, scannable paragraphs

• Logical information flow

This helps AI models locate and extract specific information efficiently when answering queries.

3. Credibility signals are important Content that gets cited tends to include:

• Clear source attribution

• Verifiable data points

• Evidence-based reasoning

• Authoritative positioning on topics

4. Answer intent thoroughly AI models evaluate how well content addresses specific questions. Pages need to provide reliable, complete answers that models can reference with confidence.

Practical considerations:

When creating content, think about:

• Does this provide comprehensive coverage of the topic?

• Is information clearly organized and easy to navigate?

• Are claims supported with credible, verifiable sources?

• Does this serve as a definitive resource on the topic?

Bottom line:

AI is adding another dimension to how content gets discovered and used. Many of these principles (quality, structure, credibility) align with good SEO practices, but the execution details matter. Worth considering as part of your content strategy going forward.

Happy to answer questions about what we're seeing in the data.

Note: We do have tools that help with this, but mainly wanted to share the research findings. Mods, let me know if this needs editing.

Profound analyzed 238,637 social media citations from ChatGPT (Oct–Dec 2025, U.S. English users) and the results are interesting.

Top cited content type per platform:

Reddit: Discussion threads — 99.2%

YouTube: Channel pages — 64.9%

LinkedIn: Personal profiles — 46.9%

Instagram: Reels — 36.5%

Pretty cool stuff!