From Ranking to Reasoning: Philosophies Driving GEO Brand Presence Tools via @sejournal, @Dixon_Jones

Since the turn of the Millennium, marketers have mastered the science of search engine optimization.

We learned the “rules” of ranking, the art of the backlink, and the rhythm of the algorithm. But, the ground has shifted to generative engine optimization (GEO).

The era of the 10 blue links is giving way to the age of the single, synthesized answer, delivered by large language models (LLMs) that act as conversational partners.

The new challenge isn’t about ranking; it’s about reasoning. How do we ensure our brand is not just mentioned, but accurately understood and favorably represented by the ghost in the machine?

This question has ignited a new arms race, spawning a diverse ecosystem of tools built on different philosophies. Even the words to describe these tools are part of the battle: “GEO“, “GSE”, “AIO“, “AISEO”, just more “SEO.” The list of abbreviations continues to grow.

But, behind the tools, different philosophies and approaches are emerging. Understanding these philosophies is the first step toward moving from a reactive monitoring posture to a proactive strategy of influence.

School Of Thought 1: The Evolution Of Eavesdropping – Prompt-Based Visibility Monitoring

The most intuitive approach for many SEO professionals is an evolution of what we already know: tracking.

This category of tools essentially “eavesdrops” on LLMs by systematically testing them with a high volume of prompts to see what they say.

This school has three main branches:

The Vibe Coders

It is not hard, these days, to create a program that simply runs a prompt for you and stores the answer. There are myriad weekend keyboard warriors with offerings.

For some, this may be all you need, but the concern would be that these tools do not have a defensible offering. If everyone can do it, how do you stop everyone from building their own?

The VC Funded Mention Trackers

Tools like Peec.ai, TryProfound, and many more focus on measuring a brand’s “share of voice” within AI conversations.

They track how often a brand is cited in response to specific queries, often providing a percentage-based visibility score against competitors.

TryProfound adds another layer by analyzing hundreds of millions of user-AI interactions, attempting to map the questions people are asking, not just the answers they receive.

This approach provides valuable data on brand awareness and presence in real-world use cases.

The Incumbents’ Pivot

The major players in SEO – Semrush, Ahrefs, seoClarity, Conductor – are rapidly augmenting their existing platforms. They are integrating AI tracking into their familiar, keyword-centric dashboards.

With features like Ahrefs’ Brand Radar or Semrush’s AI Toolkit, they allow marketers to track their brand’s visibility or mentions for their target keywords, but now within environments like Google’s AI Overviews, ChatGPT, or Perplexity.

This is a logical and powerful extension of their current offerings, allowing teams to manage SEO and what many are calling generative engine optimization (GEO) from a single hub.

The core value here is observational. It answers the question, “Are we being talked about?” However, it’s less effective at answering “Why?” or “How do we change the conversation?”.

I have also done some maths on how many queries a database might need to be able to have enough prompt volume to be statistically useful and (with the help of Claude) came up with a database requirement of 1-5 billion prompt responses.

This, if achievable, will certainly have cost implications that are already reflected in the offerings.

School Of Thought 2: Shaping The Digital Soul – Foundational Knowledge Analysis

A more radical approach posits that tracking outputs is like trying to predict the weather by looking out the window. To truly have an effect, you must understand the underlying atmospheric systems.

This philosophy isn’t concerned with the output of any single prompt, but with the LLM’s foundational, internal “knowledge” about a brand and its relationship to the wider world.

GEO tools in this category, most notably Waikay.io and, increasingly, Conductor, operate on this deeper level. They work to map the LLM’s understanding of entities and concepts.

As an expert in Waikay’s methodology, I can detail the process, which provides the “clear bridge” from analysis to action:

1. It Starts With A Topic, Not A Keyword

The analysis begins with a broad business concept, such as “Cloud storage for enterprise” or “Sustainable luxury travel.”

2. Mapping The Knowledge Graph

Waikay uses its own proprietary Knowledge Graph and Named Entity Recognition (NER) algorithms to first understand the universe of entities related to that topic.

What are the key features, competing brands, influential people, and core concepts that define this space?

3. Auditing The LLM’s Brain

Using controlled API calls, it then queries the LLM to discover not just what it says, but what it knows.

Does the LLM associate your brand with the most important features of that topic? Does it understand your position relative to competitors? Does it harbor factual inaccuracies or confuse your brand with another?

4. Generating An Action Plan

The output isn’t a dashboard of mentions; it’s a strategic roadmap.

For example, the analysis might reveal: “The LLM understands our competitor’s brand is for ‘enterprise clients,’ but sees our brand as ‘for small business,’ which is incorrect.”

The “clear bridge” is the resulting strategy: to develop and promote content (press releases, technical documentation, case studies) that explicitly and authoritatively forges the entity association between your brand and “enterprise clients.”

This approach aims to permanently upgrade the LLM’s core knowledge, making positive and accurate brand representation a natural outcome across a near-infinite number of future prompts, rather than just the ones being tracked.

The Intellectual Divide: Nuances And Necessary Critiques

A non-biased view requires acknowledging the trade-offs. Neither approach is a silver bullet.

The Prompt-Based method, for all its data, is inherently reactive. It can feel like playing a game of “whack-a-mole,” where you’re constantly chasing the outputs of a system whose internal logic remains a mystery.

The sheer scale of possible prompts means you can never truly have a complete picture.

Conversely, the Foundational approach is not without its own valid critiques:

• The Black Box Problem: Where proprietary data is not public, the accuracy and methodology are not easily open to third-party scrutiny. Clients must trust that the tool’s definition of a topic’s entity-space is correct and comprehensive.
• The “Clean Room” Conundrum: This approach primarily uses APIs for its analysis. This has the significant advantage of removing the personalization biases that a logged-in user experiences, providing a look at the LLM’s “base” knowledge. However, it can also be a weakness. It may lose focus on the specific context of a target audience, whose conversational history and user data can and do lead to different, highly personalized AI outputs.

Conclusion: The Journey From Monitoring To Mastery

The emergence of these generative engine optimization tools signals a critical maturation in our industry.

We are moving beyond the simple question of “Did the AI mention us?” to the far more sophisticated and strategic question of “Does the AI understand us?”

Choosing a tool is less important than understanding the philosophy you’re buying into.

A reactive, monitoring strategy may be sufficient for some, but a proactive strategy of shaping the LLM’s core knowledge is where the durable competitive advantage will be forged.

The ultimate goal is not merely to track your brand’s reflection in the AI’s output, but to become an indispensable part of the AI’s digital soul.

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AI As Your Marketing Co-Pilot: How To Effectively Leverage LLMs In SEO & Content via @sejournal, @cshel

I remember seeing those “God is my co-pilot” bumper stickers since I was old enough to read them.

I was a precocious little agnostic, so they always struck me as weird. God can’t be your co-pilot because God isn’t a physical manifestation of someone who can help you drive a car.

I eventually figured out that “God is my co-pilot” was less a literal statement and more a declaration of faith that there is an omniscient presence available to help you navigate life’s construction zones (if you believe, anyway).

So, fast forward to 2025, and marketers have a new omniscient presence that they can put their faith in. Something that seems equally all-knowing but perhaps a little more … unpredictable.

AI.

Large language models (LLMs) – like ChatGPT, Claude, Gemini – feel delightfully divine when you first try them. They answer instantly, confidently, and often with an authority that makes you wonder if they do know everything.

But, spend enough time with these tools, and you discover something unsettling: AI isn’t just your god-like guide. It can also act like the devil, gleefully granting your wishes exactly as asked – and letting you suffer the consequences.

This is why the healthiest way to think of AI in your SEO and content workflows is as a co-pilot. Not God. Not Lucifer. But, a powerful partner that can elevate your work, if you exercise your free will (and make good choices).

The God-Like Qualities Of AI

There’s a reason AI feels god-like in a marketing context:

• It seems omnipresent, embedded in your search results, your content management system (CMS), your analytics.
• It delivers answers instantly, with confidence and authority.
• It processes far more data than any human ever could, instantly finding patterns we mere mortals miss on the first (or third) pass.

Ask it to draft a content brief, summarize competitive search engine results pages (SERPs), generate topic clusters, or even shape a brand narrative – and it performs in seconds what would have taken you hours.

That kind of power can feel miraculous.

But, just as theologians remind us that God’s will is mysterious and not always aligned with ours, LLMs work on their own unknowable internal logic.

The outputs may not match your intent. The answer may not come in the form you wanted. And you may not even fully grasp why it chose the answer it did.

The Devilish Side Of AI

On the flip side, AI can also be a trickster: seductive, transactional, and literal. It will grant you exactly what you wish for – and sometimes that’s the worst thing possible.

When you prompt an LLM poorly, you’re effectively making a deal with the devil. The model will fulfill your request to the letter, even if what you asked was misguided, incomplete, or poorly articulated.

The result? Content that’s technically correct but off-brand, off-tone, or even factually wrong – yet delivered with such confidence it lulls you into publishing it.

The moral: Be careful what you ask for. The clarity of your prompt determines the quality of your output.

What AI Is Good At

When treated as a co-pilot, not as a god, AI can supercharge your workflow:

Research & Insights

• Competitive landscape analyses.
• SERP gap identification.
• Tracking how competitors frame their unique value propositions.
• Summarizing multiple opinion pieces or reviews into one clear insight.
• Identifying overlooked audience segments based on forums and social media discussions.

Content Ideation & Briefing

• Generating alternative angles on stale topics: e.g., turning “best practices” into “common mistakes” or “myths to avoid.”
• Rewriting existing briefs to prioritize experience, expertise, authoritativeness, and trustworthiness (E-E-A-T) signals
• Drafting Q&A content by scanning customer service transcripts or Reddit threads.
• Suggesting specific examples or metaphors to make dry topics more engaging.

Narrative Shaping & Messaging

• Reworking messaging for different formats: a LinkedIn post, an email subject line, and a webinar title – all aligned.
• Auditing your current messaging to highlight jargon and suggest plain-language alternatives.
• Helping articulate your brand’s point of view in ways that differentiate it from competitors.
• Stress-testing your messaging by generating “devil’s advocate” objections you can preemptively address.

Workflow Enhancements

• Drafting a competitive heat map: strengths, weaknesses, opportunities, threats – with citations.
• Organizing customer testimonials into themed categories and crafting pull quotes.
• Generating follow-up email sequences based on webinar transcripts or meeting notes.
• Converting white papers into tweet threads, infographic outlines, and video scripts.

It’s like an intern with infinite energy and decent taste – incredibly helpful, but still in need of supervision.

What AI Is Not Good At

Don’t confuse the fluency of AI with wisdom. Here’s where it stumbles:

Judgment & Nuance

It doesn’t understand your brand’s unique sensibility, your audience’s emotional context, or when not to say something. You have to give it that context and direction. You cannot assume it will figure it out.

Accuracy & Truth

It is still prone to “hallucinations” – confidently wrong statements presented as fact.

We have limited understanding of why this happens, but it is so frequent that you almost have to assume there are at least a few hallucinations in the output somewhere.

Accountability

It cannot make decisions, nor does it bear the consequences of your choices. That’s on you.

In short, AI lacks your free will. And free will is what allows you to question, interpret, and choose what to do with its suggestions.

The Co-Pilot Mindset: Free Will Wins

To work effectively with your AI co-pilot, you need to strike the right balance between trust and control.

Here’s how:

Stay In The Pilot’s Seat

Never hand over full control. You’re still ultimately responsible for the vehicle.

Treat AI as a partner – or maybe not even a full partner, more like an exceptionally bright and quick research assistant – but never a replacement for you in any equation.

Be Precise In Your Prompts

Don’t assume it “knows what you mean.” Giving the AI instructions is like giving instructions to a particularly clever child who enjoys maliciously complying with your orders, except the AI doesn’t actually experience the joy.

You need to articulate your expectations clearly: format, tone, audience, and purpose. Add as much context and as many constraints as you can. The more data points and context you can provide, the better the outputs will be.

Use It To Accelerate, Not Replace

AI can speed up research, help shape narratives, and generate ideas, but it can’t replace your expertise or final judgment.

Review & Revise

Never, never, never, never publish output unedited. Always apply your brand’s perspective, always fact-check, and always ensure alignment with your goals.

Read everything you’re about to publish carefully. It’s okay to trust, but always verify.

Here’s an example of how that looks in practice:

I recently took a client’s complete keyword ranking report – not just the terms they were tracking, but every single ranking URL and query – and filtered out any URL already on page 1.

Then, I narrowed the data to just rankings in positions 11-20 (to keep it manageable) and fed that into an LLM.

I asked it to estimate the potential lift in organic traffic if each term improved to position 1 and to rank the list by estimated lift, highest to lowest.

But, I also gave the LLM context about the client’s business, explaining what kinds of customers and services were most valuable to them.

Then, I asked the model to highlight the keywords that made the most business sense for this client, because not every keyword you rank for is one you actually want to rank for.

With that context, the LLM was able to match keyword intent to the client’s goals and call out the terms that aligned with their business priorities.

In just minutes, I had a prioritized roadmap of high-impact, high-fit opportunities – something that would have taken hours to produce manually.

Practical Ways To Work With AI

Here are some more actionable ways you can incorporate AI into your workflow effectively:

Research Smarter And Faster

• Create a competitive matrix with links and pros/cons.
• Summarize customer sentiment across reviews, highlighting recurring pain points.
• Surface conflicting expert opinions to inform balanced thought leadership pieces.
• Forecast upcoming trends based on chatter in niche forums and early adopters.

Build Better Briefs

• Include competitive positioning suggestions in briefs, not just keywords.
• Add tone-of-voice examples aligned to audience segments.
• Incorporate real data sources and reference points to help writers anchor their copy.
• Generate sample social captions to support a campaign.

Strengthen Your Messaging

• Stress-test a headline by generating objections and counterpoints.
• Rewrite complex product descriptions into benefit-driven language for different audiences.
• Propose alternate positioning statements for product launches or rebrands.
• Audit your FAQ section to make it more conversational and AI-friendly.

Repurpose And Expand Content

• Turn webinar transcripts into ebooks, blog series, and email drips.
• Extract key insights from research reports to create shareable social graphics.
• Draft SEO-friendly meta descriptions and titles for old content.
• Identify missed opportunities in evergreen content for updates or expansion.

AI can do so much more than just “help you ideate.” It can help you uncover blind spots, repurpose assets, and deepen your strategic thinking, but only when you stay in the driver’s seat to guide and refine the outputs.

Final Thought: You, And Only You, Are The Pilot

I think we tend to treat our collective relationship with AI the same way we look at religion – you’re either a believer or an atheist.

Some have complete faith and trust it without question, while others reject it entirely and are convinced there is nothing there to believe in. The truth is somewhere in the middle (as it often is).

AI can be a powerful, tireless, but imperfect partner. It can help carry and manage heavy mental loads, work with you to map out routes and decide on destinations, but it can not take responsibility for driving the car. That’s got to be on you.

Your free will – your ability to keep your hands on the wheel – is what ensures the journey ends where you intended. If you actually let go, you’re certainly going to crash. You’re asking for assistance, not a magical autopilot.

So, go ahead: Let AI ride shotgun and keep your hands at 10 and two, where they belong.

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