Tag: AI SEO

Unlocking SEO Success with Vibe Coding: Transform Your Strategy
I’ve discovered that the biggest SEO gains now come from interactive experiences that immediately address user intent and remove friction.

SEO was once heavily reliant on external factors, especially developer support and waiting on roadmaps that promised features “maybe next quarter.”

If I needed a new page template, a calculator, or even an interactive component, I had to wait. But that’s no longer the case.

Nowadays, if you’re involved in SEO or GEO and haven’t explored vibe coding, you might be hindering your potential impact.

Vibe Coding: Shifting SEO Power Dynamics

Not long ago, creating tools like calculators or widgets involved lengthy processes, but now I’ve used AI to build dozens of apps without needing a developer.

Some tools are basic and others not visually appealing, but they’re effective and drive thousands of organic visits monthly.

Pages centered around these tools are outperforming traditional content competitors.

What’s truly transformative is that my SEO team is now adept at building tools independently, which empowers us to achieve our goals faster.

We can test ideas instantly and utilize developer skills for more complex tasks like scaling and infrastructure.

There’s a significant sense of accomplishment when creating and releasing a tool that consistently attracts traffic.

It’s not about sophistication; it’s about building effective tools.

Engage Directly: From User Personas to Conversations

The traditional approach says to identify and cater to user personas. But few explain how to present that effectively.
- Recognize user personas.
- Pinpoint their challenges.
- Create content to address those challenges.
Previously, SEO relied heavily on text targeting personas, which is now outdated.

Instead, we should let users self-identify to show the most relevant content.
- A vibe-coded component with tabs for different personas.
- Each tab reveals content tailored to that persona.
For instance, Majorca airport transfers differ greatly between family travelers and solo adventurers.

Families care about safety and child-friendly options, visible only when their tab is selected.

SEO strategies now harness data from sources like Google Search Console to directly address these needs.

The component was strategically coded to enhance immediate intent satisfaction.

This mirrors AI platforms’ approach: segmented, persona-aware, and intent-driven.

Harnessing Traffic through Tool-Only Categories

In a personal project, I launched a Tools category with ten pages of simple, effective tools like calculators and count-down timers.
- Calculators.
- Checklists.
- Calendars.
- Countdown timers.
- AI generators.
Each page’s centerpiece is its tool, supported by components addressing additional queries.

The impact? Over 5,000 clicks in two months, even with seasonal variations.

UI: A Powerful Ranking Factor

SEO capabilities have expanded, but creativity remains essential.

Visual presentation is a highly underrated SEO asset today.

Merely producing text is insufficient. Instantly fulfilling intent through UI is key.
- Two calculator pages have added significant monthly sessions.
- A tool ranked in the top three within days for a government query.
- Pages rank off-season thanks to superior UI.
Where others list information, I offer interactive user engagement.
- Eligibility calculators.
- Countdown timers.
- Dynamic tables.
- Visual comparisons.
Text backs up the tool rather than being the main attraction.

SEO Done Right, Quickly

I published a page targeting a Greek government program, outshining heavy-text competitors.

We introduced:
- An eligibility tool.
- A transparent algorithm explanation.
- Tips to avoid application errors.
- Historical program updates.
- An application walkthrough.
The page was promptly tagged and marked up, achieving a first-page ranking within three days and generating substantial clicks.

Solving problems better than anyone else shortens the typical SEO timeline.

Maximize SEO and PR with Tools

Tools can drive traffic or act as valuable digital PR assets.

A due date calculator or baby name generator could turn into a major PR opportunity.

A modern tool addressing real needs, outshining SERP features, can become the interface where SEO and PR beacons meet.

Uncovering Tool Page Opportunities with Ease

SEO tools’ MCP servers now make discovering tool ideas from search demand a breeze, letting me validate and launch swiftly.

This method has significantly sped up my tool page creation process compared to traditional methods.

We’re moving into an era where ideation, validation, and action can occur in days, reducing project duration considerably.

The Paradigm Shift in SEO

SEO has evolved beyond long-form content, demanding fast intent fulfillment and seamless user experiences.

Embracing vibe coding can accelerate development and provide a competitive edge. Building interactive elements, not just content, is crucial for modern SEO success.

Inspired by this post on Search Engine Land.
May 12, 2026
Mastering Prompt-Level SEO for AI Search: A Guide to Experiments
As someone deeply invested in the world of AI and SEO, I’ve seen firsthand how important it is to optimize brand visibility in AI-generated responses. More and more, people are leaning on these AI models to get answers, recommendations, and even travel tips.

Imagine if your brand isn’t popping up in these responses? It’s a bit worrying, right? But here’s the big question—can we actually sway these outcomes? And, crucially, what strategies can improve your brand’s presence and visibility?

This is where structured experimentation truly shines. Unlike haphazard strategies, prompt-level SEO demands repeatable testing frameworks to pinpoint what really drives those AI responses.

Build prompt-level SEO tests with a hypothesis framework

There are no shortages of tips on boosting your brand’s AI presence. However, experimentation is the only way to find what truly resonates with your industry and your brand.

To this end, I use hypothesis-driven testing to structure experiments for my brands. It’s a systematic approach, one we can replicate across various tests and scenarios.

This structure breaks down into three parts: if, then, because.
- If: Establish your hypothesis: what action will be taken?
  - “If we include more granular product specifications in our content.”
- Then: Predict the result of executing the hypothesis.
  - “Then we anticipate our brand appearing in more product-specific prompts.”
- Because: Lay out why you believe this outcome will happen.
  - “Because AI models prioritize detailed and specific information in their responses.”
By sticking to this framework, you not only think through each test carefully but can later verify if specific elements have been previously tested, what theories were applied, and what results emerged. It’s beneficial, especially as the AI landscape evolves.

After all, as the AI model world changes, the validity of the test elements may merely shift—altering the “because” portion of our framework.

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Key considerations before running prompt-level SEO tests

Before jumping into best practices for testing, here are some essential considerations for running these experiments:
- Model updates: AI models are frequently updated. As models transition from versions like 4.1 to 4.2, revisit your results—understand how these updates affect both inputs and outputs.
- Prompt drift: Have you ever rerun an identical prompt twice on the same day? Often, the outcomes vary. Repeating prompts consecutively helps establish a real baseline. It’s quite similar to the variability seen in personalized search results. While brands adjust to this variance, certain averages become the benchmark, and prompt testing functions much the same way.
With the framework in mind, let’s explore the core elements of tests applicable to prompt-specific scenarios.

How to isolate variables: A methodological approach

Creating reliable prompt-level SEO experiments involves isolating a single causal variable. This ensures that any changes in AI responses are confidently linked to a particular action.

1. Content changes

When you’re experimenting with content modifications, ensure the changes are precise. A common mistake is updating too much simultaneously (for example, changing a product description while altering the page’s schema).
- Best practice — The single-paragraph swap: Focus on changing a single, specific piece of text on the page, such as a product description or an FAQ answer.
- Methodology: For proper isolation, conduct A/B testing with a control page that holds the original content and a test page with the modified content. Design the prompt to target the changed information. Track the brand’s inclusion rate and response position over a set period, like seven days.
2. Structured data

Structured data, or schema, delivers clear signals to search engines and AI models. Testing this means isolating the schema update as the only change to the page.
- Variable isolation: Experiment by adding new properties (such as brand, model, or offer details) without changing the visible HTML text, isolating the machine-readable layer’s impact.
- Specific experiment — FAQ schema: A highly successful strategy involves adding FAQ schema to pages that already have Q&A sections in HTML, indicating the explicit schema markup’s effect on AI ingestion.
3. Before-and-after prompt testing

This method establishes a strict baseline, introduces a change, and then repeats the prompt query. It functions as a critical control technique when true A/B testing on the AI model isn’t feasible.
Protocol
- Phase 1 (baseline): Execute 5-10 target prompts daily over seven consecutive days to develop a comprehensive average of inclusion and position-in-response, also accounting for prompt drift.
  - Action: Implement the isolated change, such as a content or schema update.
- Phase 2 (measurement): Re-run the identical set of prompts daily over the next seven days.
  - Analysis: Compare the average inclusion rate and position from Phase 1 to Phase 2, a method essential for initial presence score analysis, such as using 25 keywords and prompts across three buckets totaling 75 queries.
Encouraging reproducible experiments

Given the rapid development of AI models and limited model insights, reproducibility can be a challenge. However, the aim is to transition from single successful experiments to constructing a durable methodology.

Mandatory frameworks

Ensure every test is meticulously documented using the “if, then, because” hypothesis structure. This process archives the premise, action, and expected result, enabling future teams to quickly assess a test’s ongoing relevance as AI models change and evolve.

Technical integrity
- Version control: Record the specific model and version used in tests (e.g., “Gemini 4.1.2”), which simplifies comparison following a model update.
- Prompt libraries: Maintain a well-organized, time-stamped collection of exact prompt queries used during baseline and measurement stages, tracking inclusion rate, position-in-response, and sentiment/framing for each inquiry.
Infrastructure consistency

Clearly define the testing environment (e.g., clear browser cache, no login state) and, whenever possible, use APIs or synthetic testing platforms to control for personalization and location bias, similar to managing personalized search results in traditional SEO.

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Moving beyond one-off wins in AI search

The essence of effective prompt-level SEO lies in its rigorous methodology. By embracing a hypothesis-driven mindset, precisely isolating variables, and establishing robust before-and-after testing protocols, you can leave speculation behind.

Following these guidelines, we can pave a clear path toward significantly influencing AI model responses through controlled, thoroughly documented, and reproducible experiments.

Inspired by this post on Search Engine Land.
May 8, 2026
SEO in 2026: Building Recognition Over Rankings

As I see it, the focus of SEO in 2026 has shifted dramatically. Now, recognition has taken precedence over traditional rankings. It’s fascinating how visibility today is essential and influenced by factors like authority, brand presence, and clarity of information across the entire web, not just our position on the search results page.

For almost two decades, our main goal was to secure the top spot on search results. It felt like a game where rankings equaled visibility and traffic. But now, that premise is evolving faster than ever, reshaping the very essence of SEO.

AI overviews and platforms are altering how people interact with online information. We’re noticing zero-click searches becoming the norm, demanding a shift from traditional tactics to a fresh perspective where recognition is the ultimate goal.

SEO has always followed the algorithm’s lead, adapting to its signals. Yet, this time, the change feels deeper. I find myself questioning how we can ensure our brand is preferred in a conversation, moving beyond just ranking well.

With AI transforming what searchers see, our high-ranking pages need more than just good positioning. They require acknowledged authority — being known, cited, and trusted beyond our own domains. This approach ensures that when AI platforms provide answers, our brand stands recognized.

User behavior is also shifting. I see more users getting their answers directly from AI without even clicking further. This world demands that our strategy aligns not just with ranking questions, but with how our brand becomes the preferred conversation choice.

It’s crucial to understand how AI ‘chooses’ which brands to recognize. It requires a brand’s consistent presence across various platforms and discussions, beyond just search engine results. It’s about accumulating recognition over time and ensuring we’re part of those trusted domains.

Recognition also involves having clear entity presence, being cited in meaningful contexts, and ensuring authority across relevant topics. For me, this extends beyond just SEO; it’s building our presence across the vast digital landscape.

True recognition requires a deliberate and strategic approach. It might be slower to achieve but offers a long-term durable advantage. It’s about setting ourselves up to become respected authorities that AI systems—and users—genuinely trust.

Inspired by this post on Search Engine Land.

May 8, 2026
AI SEO: Transforming Marketing Beyond Lazy Strategies

Over the years, I’ve noticed how digital marketing has settled into a predictable routine. It spans across various channels like SEO, content marketing, social media, and digital advertising. Yet, many of us relied too heavily on a familiar core strategy, often ignoring the potential of using every available channel.
This predictability was comforting. It allowed marketing teams, including mine, to stick to what worked, refining execution within a known framework. However, AI search has upended this comfort, exposing our inconsistencies. To truly succeed with AI SEO, it’s clear that I need to adopt a much broader strategy.
Over the last 15 to 20 years, I’ve observed how digital marketing comfortably fit into a predictable rhythm, with each channel having a designated role.
Content marketing, social media, SEO, and paid advertising followed habitual strategies. But this lack of variation led to a form of laziness in our approach.
This structure offered results, so we let the broader strategies slip away.
The issue? It gave us a false sense of security. We should have employed broader strategies all along, as they now drive real visibility in AI search.
AI has reshaped digital marketing, changing user search behavior and how brands are evaluated.
Traditional search relied heavily on algorithms and singular sources, whereas AI taps into multiple inputs across numerous sources.
These sources ought to be part of your marketing arsenal—representing your brand across social media, third-party directories, press releases, and more. In this new system, your website is just one element among many sources AI uses to comprehend your brand.
One of the most significant changes AI has introduced is how it has expanded the digital marketing landscape beyond the website. While having a robust website is crucial, it’s part of a much larger ecosystem now. The marketing strategy must adapt to this expansive landscape.
In the past, maximizing website visibility was often enough to yield results. However, relying solely on this approach no longer suffices. AI aggregates data from a wide range of sources, from articles and brand mentions to third-party profiles and published content, shaping its understanding of who you are.
Focusing exclusively on the website restricts AI’s ability to locate and understand your brand.
Most marketing programs, especially those established before AI’s time, fall short here. To modernize, it’s vital for a brand to be visible across a more extensive range.
AI prefers brands that establish an intentional online presence, showing up with purpose across the internet.
A fragmented marketing approach, which worked in the past, now falls short. Previously, each successful channel felt effective and met our goals, but AI demands more. It looks for consistent messaging and expertise, linking various online signals to assess your brand’s presence.
When these signals are aligned, your brand’s visibility in AI search improves. Inconsistent or weak broader presence translates to weaker visibility.
Lazy marketing approaches—sticking to separate channels using the same old tactics—are now exposed. This approach may have yielded results once, but those days are numbered. It’s crucial now to go beyond that—to present your brand on multiple platforms, so AI can find you.
If your competitors enhance their presence, failure to do the same will leave you behind as they occupy more space in AI-generated responses.
As AI exposes any inconsistencies, it’s time to transition into the era of AI search.
It’s essential now to transition beyond older models and adopt newer strategies suitable for digital marketing. The tactics that always worked like press releases, directory listings, and marketing beyond just your website, should have been in use all along.
AI search doesn’t rewrite marketing rules; it enforces the importance of a comprehensive strategy. This means we can’t afford to do less anymore.

Inspired by this post on Search Engine Land.

May 7, 2026
How AI is Revolutionizing Microsoft’s Search Indexing

I recently came across an intriguing blog post by Microsoft Bing that delves into how AI is transforming the traditional concept of search indexing into something far more sophisticated. Bing has been focusing on enhancing factual accuracy, attribution, and confidence levels before AI-driven answers are generated.

The transition from page ranking to supporting AI-generated answers is reshaping how search engines operate. According to Bing’s latest insights, AI requires a more complex indexing system compared to the conventional web searches we’re used to.

Traditional Search vs. Grounding Systems

Microsoft highlighted a key difference: while traditional searches allow users the opportunity to self-correct, AI systems must derive more substantial evidence since they generate definitive answers.

Grounding systems focus on verifiable facts with transparent sourcing, crafting combined answers where errors could compound through different reasoning steps.

They shared this illustrative table:

What Sets Them Apart

Traditional algorithms optimize for relevance. In contrast, AI grounding evaluates whether information is correct, recent, well-sourced, and comprehensive enough to support an answer. It also considers whether the essence of a page endures through transformations and chunking.

Stale Content Concerns

Microsoft pointed out that outdated content poses a unique risk to AI-generated answers. Unlike traditional ranking, outdated information can lead to inaccurate AI results.

Handling Contradictions

In traditional search, a hierarchy can be established by ranking sources for users to choose trusted information. Grounding systems, however, must identify conflicting data and deliberate their consolidation into a singular response.

The Complexity of Retrieval

Unlike a one-time query in traditional search, AI systems might fetch information multiple times, refining previous results, and re-evaluating confidence before shaping an answer.

Measuring Indexing Quality

While the quality of conventional search indexes centers on ranking performance, grounding systems require assessment of factual accuracy, source integrity, freshness, and conflict recognition. Microsoft notes the ongoing journey in refining these measurements.

Complementing, Not Replacing Search

Grounding isn’t intended to replace search. Rather, it supplements existing systems with a focus on evidence quality and attribution, determining if AI should refrain from responding when necessary.

Why This Matters

For decades, search indexes have guided users to relevant web pages. Today, AI grounding is about ensuring the data it uses stands the test of reliability. This evolution demands that brands and publishers focus on creating data AI can leverage with greater certainty.

For More Insights read the detailed blog post, Evolving Role of the Index: From Ranking Pages to Supporting Answers.

Inspired by this post on Search Engine Land.

May 6, 2026
Is Your WordPress Blocking AI Bots? Discover the Hidden Barriers

When I first looked at my SEO data, everything seemed perfectly fine. All metrics from Google Search Console, traffic, and indexing were normal without any red flags. But then, I decided to dig deeper using Scrunch, our AI citation monitoring tool, to examine the platform presence for searchinfluence.com over the past 30 days.

Here’s what I found: Google AI Mode showed a presence of 37.8%, Copilot at 22.2%, Google Gemini at 16.3%, ChatGPT at 9.6%, and Perplexity at 7.8%. Alarmingly, both Claude and Meta AI were at 0.0%.

Two platforms had zero presence. Given that every crawler reads the same site, differences in content quality or topical authority couldn’t explain this discrepancy. The only factor that varied was crawler access.

To understand this further, I analyzed seven days of Cloudflare logs and discovered 29,099 bot requests, with 65.8% involving AI bots. The requests rate-limited with HTTP 429, or “too many requests,” were interestingly varied by bot user-agent.

Training crawlers that make bulk requests are throttled, while user-facing crawlers that mimic human pacing during live queries aren’t. For example, ClaudeBot made 20,583 crawl requests for each referral returned.

My assumption was that the 429 errors originated from Cloudflare, perhaps due to a web application firewall (WAF) or security plugin interference. I went down a rabbit hole investigating multiple layers. It was time-consuming and ultimately unnecessary.

The truth emerged when I performed a reproduction test using curl requests, revealing that the block was based on user-agent, not path or rate. The realization hit when I discovered the x-powered-by header: WP Engine hosted our site, and the block came from their platform infrastructure.

I then tested other AI bot UAs and crafted a fingerprint for each, discovering that the blocklist was outdated. While some bots were blocked, others like Common Crawl passed through unaffected.

In conclusion, while WP Engine’s firewall, documented on their support page, was intended as a security measure, it wasn’t transparent to customers. Identifying these blocks requires specific diagnostic steps, and the process taught me much about managed hosting’s hidden layers.

Inspired by this post on Search Engine Land.

May 6, 2026
Unlocking Search Success: Unifying Strategies for 2026 Growth

You know what I’m starting to realize? Our customers see the entire search engine results page (SERP). So, if they do, shouldn’t we?
Back in February 2024, Gartner predicted a 25% decrease in traditional search volume by 2026. But guess what? That didn’t happen. Google’s search revenue soared by 17% year-over-year, hitting over $63 billion in just the last quarter of 2025. While query volume is surging, clicks per search are on the decline. It’s like the pie got bigger, but the slices are being divvied up differently, and many of us are still optimizing for that old pie.
I have a question for you: Are we stuck rifling through endless spreadsheets of organic keyword rankings like it’s still 2003? Our customers don’t care about where they get their answers; they just want them to be trustworthy. And they’re finding those answers across a wide array of platforms that our standard rank trackers might not even be aware of.
If our organic, paid, and AI search strategies are operating in separate silos, we might be optimizing for a search experience that’s obsolete.
What Search Really Looks Like Today
Go ahead and Google “best tax software” right now. I’ll wait.
Notice the variety on just one results page: top sponsored ads, an AI Overview citation, a Reddit thread (because people trust real people more than brands), organic listings from CNET and H&R Block, a video carousel, discussion forum links, a product carousel with prices, more sponsored results at the bottom, and a “People also search for” section directing the next inquiry.
This is one search with one keyword, and nobody truly owns it.
Reflect on how different folks use that page. I’ll scroll right to the Reddit thread, seeking genuine human recommendations. My dad clicks the first sponsored ad, trusting Google to display the best option up top. Someone else might read the AI Overview and feel content enough with the answer to avoid further clicking. A fourth person might watch that Smart Family Money video and depart satisfied.
Same query, four distinct paths, four different “winners.” As a brand, if we’re celebrating being third in organic ranking on this page, we should realize that most of the attention and user engagement may be happening beyond those blue links.
That’s why I emphasize understanding the total SERP experience. If our customers are seeing the whole picture, shouldn’t we?
The AI Layer Changes the Equation
AI Overviews now appear on around 25% to 48% of Google queries, according to various studies. ChatGPT processes 2.5 billion prompts daily. Perplexity’s up by 239% year over year—hard figures from platforms shaping consumer opinions about our brands. Yikes, right?
But let’s not start panicking. AI might be shifting the terrain, but it only represents less than 1% of U.S. web traffic. Google, on the other hand, drives referrals 300 times more than all AI platforms combined.
The significant transformation lies in consumer behavior. According to Wynter’s 2026 research, 68% of B2B buyers initiate their research within AI tools before heading to Google. They use ChatGPT to narrow down options, then verify them on Google. AI evaluates, Google validates, and it’s on us to convert. If we aren’t in that initial AI conversation, we’re missing the chance to be a go-to choice.
Why the Click Data is Intriguing, Not Alarming
A Search Engine Land study of 25 million organic impressions revealed that organic CTR drops by 61% when an AI Overview is present, with paid CTR plummeting by 68%.
It’s tempting to go into panic mode but don’t hit the alarm just yet.
Here’s an interesting finding: brands cited in AI Overviews experience a 35% increase in organic clicks and a 91% rise in paid clicks. The AI Overview acts as a trust signal, boosting user engagement below the overview itself.
Interestingly, ranking high in organic doesn’t automatically put you in the AI’s radar. Research by Tom Capper at Moz shows that 88% of AI Mode citations don’t appear in the organic SERPs for the same query. Organic and AI sources differ. You could be the top Google result but completely invisible in a ChatGPT response to the same query.
But here’s a glimmer of hope—traffic from AI tends to convert at quadruple the rate of organic traffic. Its audience arrives informed and ready to make decisions after preliminary evaluation in the AI space.
The Organizational Chart is the Roadblock
Most organizations have SEO reporting to content, PPC to demand gen, and AI search to no one, effectively stranding strategic coherence. BrightEdge found 54% of organizations delegate AI search solely to SEO teams, akin to entrusting your plumber with your electrical work because it’s all in the same house.
The losses here are tangible. One Performance Max campaign paid a staggering $500,000 for clicks that were coming naturally through organic referrals. Google’s studies confirm that when you’re organically ranked first, half of your paid clicks might as well have been free.
Moreover, McKinsey’s findings show a brand’s own website contributes only 5% to 10% of sources AI refers to. AI aggregates from Reddit, review sites, affiliates, and more. A top-tier SEO program might still leave you out in the cold when it comes to AI, as it’s influenced more by collective sentiment than official content.
A unified strategy works wonders. At Level, we cut acquisition costs by 18% and increased SEO leads by 22% by merging paid and organic efforts for a B2B SaaS client. Our Level Intelligence Suite connects performance signals across search surfaces, proving that compartmentalizing these efforts is a missed opportunity for synergy.
Three Audits You Can Kickstart on Monday
If you’re looking for a fast start, here are three audits using your top 20 keywords to pinpoint gaps and opportunities.
Lens 1: Check Where You’re Really Visible. Analyze your organic rankings, paid ad presence, and AI search visibility across platforms like ChatGPT, Perplexity, and Gemini. Use Semrush’s free AI visibility checker to see where you really stand.
Lens 2: Identify Unnecessary Ad Spend. Correlate your top organic rankings with active PPC bids. Begin with branded keywords, where over-expenditure from paying for organic reach is typically largest.
Lens 3: Discover AI Overlooking. Compare your organic presence with AI citations. Only 11% of domains are noted by ChatGPT and Perplexity, so strength in one area doesn’t ensure visibility in the other. Ensure your robots.txt isn’t blocking AI crawlers, or you’ll be invisible in those discussions.
This revealing diagnostic paves the way for action. I’m laying out a detailed unification framework at SMX Advanced, and I’d love to see you there.
The Window Won’t Stay Open Forever
Generative Engine Optimization (GEO) keyword difficulty currently floats between 15 and 20, far lower than traditional SEO terms, which can span 45 to 60. This disparity will soon narrow, as favored sources selected by LLMs end up being perpetually referenced.
Some companies are watching their search traffic nosedive, yet they are surging in actual business growth. These firms stopped isolating channels and started analyzing their customers’ comprehensive search journey.
We’re introducing our unified search strategy at SMX Advanced in our session titled “Organic, Paid, and AI Search: One Strategy to Rule Them All.” If you’re eager to blend your strategies into one cohesive plan, join our session or visit us at Booth #9.
Remember, the search experience we had in 2023 has evolved, and our strategies should too.

Inspired by this post on Search Engine Land.

May 5, 2026
Why AI Search Visibility is Essential for Brands Today

The way we search for information has shifted dramatically—not slowly and not slightly. I’ve witnessed firsthand the transformation in search behaviors that make AI search visibility crucial for brands seeking to remain competitive.

Brands need to adopt AI search visibility services now more than ever to ensure they’re not only visible online but also standing out in an overcrowded digital space.

With the right AI tools, brands can refine their search visibility strategies to reach target audiences more effectively, leveraging cutting-edge technologies to stay ahead of competitors.

Inspired by this post on HiGoodie Blog.

May 4, 2026
Mastering Effective SEO Agent Skills: A Personal Journey
I’ve been on a journey to develop over 10 SEO agent skills in just 34 days. Six of these succeeded on the first attempt, while the remaining four taught me invaluable lessons, especially about the overlooked importance of folder structure that many LinkedIn posts on AI SEO skills seem to miss.

The reliability of these agents isn’t about crafting superior prompts; it lies in the architecture that supports them. Here’s my blueprint for building an agent from scratch, testing it diligently, refining it, and deploying it with full confidence.

Here’s why many AI SEO skills don’t make the cut.

A typical AI SEO prompt seen on platforms like LinkedIn usually looks something like this:
```
You are an SEO expert. Analyze the following website and provide a comprehensive audit with recommendations.
```
And that’s where it ends. One simple prompt, often coupled with some formatting directions, is shared with the world. The post then earns hundreds of likes, yet the output—while polished—is often up to 40% inaccurate.

I know because I’ve been there. Initially, I tasked an agent to identify SEO issues on a website, and while it came back with 20 findings, eight were non-existent. The agent hadn’t truly visited many of the reported URLs.

Here are three key issues that doom single-prompt skills:
- No tools: The agent can’t physically verify the website; it relies on training data to guess. Queries about canonical tags, for instance, result in assumptions rather than real-time analysis of HTML.
- No verification: There’s no check on the truthfulness of output. An agent might report missing meta descriptions across 15 pages, but without verification, we don’t know if these pages are even indexed correctly or intentionally set as noindexed.
- No memory: The agent’s feedback varies wildly with each use, showing inconsistency due to the lack of a template or structured history of previous runs.
In essence, if your skill is just a prompt within a lone file, you’ve got a 50/50 chance at best.

Every agent in my system has a dedicated workspace. Consider it akin to a new employee’s desk, equipped with all necessary resources. For example, our agent designed to crawl and map website architecture works within this kind of structured environment:
```
agent-workspace/
  AGENTS.md          instructions, rules, output format
  SOUL.md            personality, principles, quality bar
  scripts/
    crawl_site.js    tool the agent calls to crawl
    parse_sitemap.sh tool to read XML sitemaps
  references/
    criteria.md      what counts as an issue vs noise
    gotchas.md       known false positives to watch for
  memory/
    runs.log         past execution history
  templates/
    output.md        expected output structure
```
The workspace includes six key components services that just one prompt couldn’t dream of covering fully.

Within AGENTS.md, I’ve articulated a meticulous methodology comprising thousands of words. Instead of a simple instruction like “crawl the site,” I detailed each step: “Start with the sitemap; if it doesn’t exist, check various routes like /sitemap.xml, /sitemap_index.xml, and robots.txt for references.”

Scripts represent the tools the agent utilizes. Instead of writing curl commands from scratch for each crawl, the agent can run node crawl_site.js -url to analyze website data, which is far more efficient and reliable.

References consist of criteria that help the agent distinguish between significant issues and noisy false positives, using a wealth of knowledge I’ve amassed over two decades.

To ensure that every execution is informed by the past, I keep meticulous logs under memory, serving as institutional knowledge that empowers consistency across agent runs.

Through templates, I outline the exact format I expect from the output, thereby maintaining high quality across multiple iterations of the same task.

Building from scratch, the first naive attempt involved simple instructions that inevitably failed when confronted with modern CDNs. By iterating and incorporating tools like crawl_site.js, enhancing with rate limiting, and tackling JavaScript rendering, I’ve honed an architecture that delivers consistent outputs across runs.

The path involves a series of iterations where each failure metamorphoses into a permanent lesson, gradually shaping a sophisticated system. This methodically structured approach ensures that what we build is not just technically proficient but measurably better with every successive run.

Inspired by this post on Search Engine Land.
May 1, 2026
Transforming SEO: A Guide to Semantic and Programmatic Success

As I dive into the world of Programmatic SEO (pSEO), I understand that many people in the industry view it with suspicion, associating it with low-quality pages and duplication. Often, it’s seen simply as replicating city names on static templates.

Google’s policies on content spam are clear: strategies that generate unoriginal content just to influence rankings will not be tolerated.

In the modern landscape, pSEO isn’t about mass page generation. Instead, I aim to address thousands of search intents with local specificity and semantic depth, achieving what isn’t possible manually.

Here, I share my blueprint for transitioning from syntax-based to semantics-based pSEO, using methods we’ve tested with major companies in Brazil.

When embarking on a pSEO project, it’s common to start with templates. Yet, this approach often misses the mark. For instance, the intent behind “Best Hotel in [Las Vegas]” differs from “Best Hotel in [Orlando],” focusing on entirely different priorities and amenities.

I leverage AI to make content more granular, ensuring that each page addresses unique travel intents rather than generic keywords. My goal isn’t just to create a thousand pages, but a thousand pages that each fulfill a specific travel need.

Before creating content, I must answer a vital question: where does my domain have authority to rank? Failed pSEO projects often miss this step, targeting areas without established authority. My solution involves deep analysis using real Google Search Console data.

Through cluster audits, priority definitions, and strategic calendar alignment, I ensure my pSEO actions enhance topical authority while addressing existing semantic gaps.

Brand consistency is a hurdle when adopting AI. By implementing context governance, I ensure AI-generated content remains true to the brand’s voice, using guidelines to prevent deviations.

For internal linking, I adopt the semantic mesh strategy to ensure that every page connects logically, directing the user through a logical journey rather than dead ends.

In practice, understanding regionalization and seasonality at scale is crucial. Ânima Educação in Brazil is a perfect case study, showing how strategic pSEO leads to precision and considerable business impact.

As I scale content, monitoring with technical SEO agents helps maintain site quality, foreseeing issues like indexing problems or high LCP in real time.

In summary, successful SEO is about integrating the efficiency of technology with the nuanced human touch to deliver timely and relevant content to users.

Inspired by this post on Search Engine Land.

May 1, 2026