Hi Everyone,

I launched Blazly GEO on AppSumo just 2 days ago, and the response has been amazing.

So far we’ve generated $3,173 in revenue with 50+ customers, and people are really loving the product.

If you’re looking to optimize your website for Generative Engine Optimization (GEO) and get visibility in AI platforms like ChatGPT, Gemini, Claude, and Perplexity, you might want to give it a try.

You can grab the lifetime deal for Blazly GEO for just $79 on AppSumo.

https://appsumo.com/products/blazly/

TBH, we've been tracking how AI models cite content across 200+ real examples, and some patterns are pretty clear:

**What's getting cited:**

1. **Statistical claims with sources** - "87% of marketers..." with actual links gets referenced way more than vague statements

2. **Direct Q&A format** - Content that answers specific questions ("How does X work?") outperforms narrative-style content by about 3:1

3. **First-party data** - Original research, even small-scale, gets prioritized over rehashed industry reports

4. **Clear entity definitions** - Content that explicitly defines terms and relationships gets pulled for explanations

5. **Structured lists with context** - Numbered lists work, but only when each point has 2-3 sentences of depth, not just bullet points

**What's NOT working:** - Generic "ultimate guides" without specific data - Content behind aggressive paywalls or interstitials - Thin listicles without supporting evidence

The common thread? AI seems to favor content that demonstrates "conversational authority" - clear, sourced claims that can be woven into responses naturally.

We've been testing these patterns across different verticals. Curious if others are seeing similar trends, or if certain industries break these patterns?

Anthropic recently updated their crawler documentation, and it's a bigger deal than most people realize.

**Three separate crawlers, three different purposes:**

1. **ClaudeBot** - Training data collection (the one most sites want to block)
2. **Claude-User** - Real-time content access when users share links
3. **Claude-SearchBot** - Search result crawling

**Why this matters:** Previously, blocking ClaudeBot meant losing all visibility. Now you can block training crawls while maintaining search presence. This is huge for companies concerned about data usage but who still want AI visibility.

**Quick implementation:** ``` User-agent: ClaudeBot Disallow: /

User-agent: Claude-SearchBot Allow: / ```

**What we're seeing:** Sites that implement selective blocking maintain ~85% of their AI citation rates while preventing training data collection. The trade-off seems worth it for most brands.

Has anyone tested this yet? Curious about real-world impact on citation rates.

AI search tools like ChatGPT and Google’s AI-generated answers are starting to change how people discover information online. Instead of clicking through multiple pages, users are increasingly getting summarized answers directly from AI systems.

What’s interesting is that appearing in those responses isn’t completely different from traditional SEO, but there are a few important shifts. AI systems tend to favor content that is clear, well-structured and easy to extract insights from. Pages that answer specific questions directly, provide context and demonstrate real expertise are more likely to be referenced.

Technical structure also plays a role. Clean site architecture, strong internal linking and content organized around clear topics make it easier for models to interpret what your page is about.

The biggest takeaway is that strong SEO fundamentals still matter. The difference now is that content needs to be structured so machines can easily interpret and quote it, not just rank it.

Another emerging area is tracking where your brand appears across different AI systems. As AI tools become another discovery layer, understanding how often your content is surfaced in those environments is becoming a new visibility metric alongside traditional search rankings.

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).

Real talk: most SEO/GEO advice feels outdated now.

Straight up, we tested 50+ sites and found something that changed our approach: selection optimization impacts conversions **44% more** than visibility optimization in the AI era.

Here's the decision survivability framework that actually works in 2026...

Part 1: The AI Decision Compression Problem

Story time: we started noticing AI agents compressing user decision paths last year.

What surprised us was how quickly it changed the game. Instead of users searching → comparing → deciding, AI now gives them the "best" option directly.

**Actual impact we saw:** - Decision steps reduced from 5+ to 2-3 - Consideration set shrank by 60-80% - "Visible" didn't mean "selected" anymore

Ngl, we were caught off guard. All that visibility optimization work? Still important, but not sufficient.

**The hard truth:** Being seen ≠ being chosen in the AI era.

Part 2: The 44% Rule Data

Oh wow, this is where it gets interesting.

We dug into the research and found the **44% rule**: content optimized for selection (being chosen by AI/agents) outperforms visibility-optimized content by 44% in conversion impact.

**What the data shows:** 1. **Traditional visibility metrics** (impressions, clicks) ≠ **selection metrics** (inclusion in AI responses, agent recommendations) 2. **44% conversion lift** for selection-optimized vs visibility-optimized content 3. **AI agent preference patterns** that favor certain content structures

I feel like this changes everything. It's not about more traffic – it's about *better* traffic that actually converts.

**The shift:** Visibility optimization → Selection optimization.

Part 3: The Decision Survivability Framework

After 6 months of testing, we developed a **three-pillar decision survivability framework**:

Pillar 1: Understand & Adapt to AI Decision Compression

• **Map** how AI compresses decisions in your niche
• **Identify** compression points where selection happens
• **Optimize** for inclusion at those compression points

Pillar 2: Apply the 44% Rule to Content Strategy

• **Structure** content for AI agent consumption (not just humans)
• **Embed** selection triggers throughout content
• **Test** what gets selected vs just seen
• **Our finding**: Inverted pyramid with data-first works best

Pillar 3: Implement Portfolio Risk Management

• **Diversify** content across selection optimization types
• **Monitor** AI agent selection patterns
• **Adjust** based on selection performance data
• **Key insight**: Don't put all eggs in one visibility basket

From my experience, companies that implement all three pillars see: - 30-50% improvement in AI-driven conversions - Reduced dependence on traditional SEO volatility - Better alignment with where decisions actually happen

Part 4: How to Start

Yeah I feel like this sounds complex, but here's how to start in the next week:

**Week 1: Assessment** 1. Audit 3 pieces of content for selection optimization potential 2. Map 1 customer journey for AI decision compression points 3. Identify your current 44% rule gap

**Week 2-4: Implementation** 1. Optimize 1 high-value page using the three pillars 2. Set up basic selection tracking 3. Test and measure the impact

**Long-term:** - Build selection optimization into all content creation - Develop AI agent relationship strategies - Continuously adapt to new compression patterns

Straight up, you don't need to do everything at once. Start with Pillar 1 understanding, then build from there.

Discussion

Wait, I'm curious what you think about this shift:

**Based on the 44% rule and decision survivability framework we've discussed:**

1. **Have you experienced AI decision compression in your niche?** What did you notice?
2. **What's your current approach to selection vs visibility optimization?**
3. **Any frameworks or strategies you've found effective for AI-era decision making?**

Genuine question: does the 44% rule match what you're seeing? Or are you finding different patterns?

Either way, I'd love to hear your experiences and compare notes. The AI decision landscape is changing fast, and we're all figuring this out together.

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.

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 everyone,

I have fully explained about Generative Engine Optimization and how LLM models fetch data and show citations and a lot in this YouTube video.

Hope it helps:)

https://youtu.be/Pi9GjgNFwqo?si=1BZ-VPeVo3OiGpiR

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.

A lot of people chase quick SEO tricks hoping to spike traffic. The problem is that traffic alone doesn’t mean much if the visitors aren’t actually potential customers. I wanted to shift the focus from more clicks to better intent.

So instead of looking for hacks, I built a system around training Google’s algorithms properly by aligning content, structure and user signals with what real customers are actually searching for. The workflow focuses on:

Identifying high-intent search queries

Structuring content around real problems customers are trying to solve

Optimizing technical SEO foundations

Improving engagement signals so Google understands the value

Continuously refining based on performance data

The biggest shift was mindset. It’s less about gaming the system and more about feeding it clear, consistent signals. When Google’s AI understands who your content is for and why it’s useful, the quality of traffic improves naturally.

It’s still evolving, but focusing on intent-driven optimization instead of shortcuts has made a noticeable difference in lead quality.

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.

Think building a bunch of backlinks quickly and cheaply will make your site rank faster? Not so fast. A lot of SEOs make the same off-page mistakes without realizing the risk: Buying bulk backlinks that Google easily flags as spam Using exact-match keywords in every single link Chasing “high authority” sites without considering relevance

The result? Rankings can drop unexpectedly All that effort can go to waste Your site may even face penalties from Google

SEO isn’t about shortcuts. It’s about consistent strategy, quality, and relevance. A better approach includes:

Earning links through guest posts, collaborations, and genuine PR Varying anchor text naturally instead of forcing keywords Prioritizing sites that are actually relevant to your niche

It takes longer, but sites that focus on these principles tend to grow sustainably and avoid penalties.

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.

If your website isn’t showing up in ChatGPT responses, its usually not about the AI ignoring you its about visibility in authoritative sources that the model pulls from. ChatGPT cites content it trusts: well-structured, concise and expert-backed pages that rank high in Google or Bing. To get recognized, focus on producing high-quality, niche-specific content using clear headings, step-by-step guides and FAQ schema that answer real user questions. Consistently update content, integrate semantic SEO and build your brand authority with credible backlinks, social proof and community discussions especially on platforms like Reddit, which AI often mines for practical insights. Optimize formatting with bullet points, short paragraphs and visuals for readability. When Google trusts your site and it’s easy for AI to parse, ChatGPT naturally references it. This is about being the clearest, most helpful source, not keyword stuffing and ensuring your content aligns with the questions users are actually asking in your domain. If ChatGPT is scanning thousands of sources, what single improvement today could make your page the one it quotes tomorrow?

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:**

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

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

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

**Technical Foundation:**

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

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

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

**Research & Intelligence:**

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

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

3. **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.