Glen Swindell

Terraboost Media

Fractional RevOps

Led revenue and marketing teams through data-driven strategies that fueled growth and improved efficiency. Optimized operations and empowered teams with streamlined processes, implementing integrated revenue, sales, and marketing initiatives to drive measurable results.

Vice President, Revenue & Marketing Operations

OrangeCore Group

Founded OrangeCore Group, a strategic advisory firm specializing in revenue growth, eCommerce, and cross-functional initiatives. Currently partnering with 3 clients across fintech, retail, and SaaS industries, delivering tailored solutions to drive revenue acceleration and operational efficiencies.

Managing Director & Founder

Various Startups

Providing support in scaling revenue with a focus on sales processes, operational efficiency, eCommerce, and payments strategies.

Revenue Growth Advisor

Pavilion

Executive Member

GTM United

Member

RevGenius

Rev Ops Community Founding Member

CASA of Denton County, Inc.

Previously served on Pulaski County CASA Board of Directors in Little Rock, Arkansas (2018-2020).

Board Member - Governance Chair

Veterati

Board Advisor & Veteran Mentor

BGiving

Fractional engagement to develop and implement their GTM motion.

Fractional Chief Revenue Officer

Verifone

Spearheaded expansion initiatives across the Americas region for mid-market & enterprise eCommerce clients, delivering omnichannel solutions that accelerated client revenue by 14% year-over-year.

VP of Americas eCommerce & Senior Sales Director

Payments & eCommerce Executive @ Verifone | Driving Innovative Payment Solutions | Fintech & SaaS Enthusiast | Servant Leader | Army Veteran

As someone who thrives on driving growth and unlocking business potential, I've spent over a decade doing just that for fintech, eCommerce, and SaaS companies. But what really sets me apart is my approach - shaped by military service, it's all about fostering relationships and aligning teams for long-term success.

At OrangeCore Group, the company I founded, we get our hands dirty partnering with businesses to optimize revenue streams, implement sales processes that actually work, and deliver customized eCommerce and payments solutions. I'm a big believer in servant leadership, so you can count on me to roll up my sleeves, mentor your team, and build an environment of trust and collaboration laser-focused on smashing goals.

Optimization is my jam. I take a no-nonsense approach to analyze bottlenecks and inefficiencies, and then implement improvements that streamline operations and boost profits. Helping organizations unleash their full potential is what fuels me - whether it's cultivating elite teams or steering the ship with bold, visionary initiatives.

When I'm not geeking out over business strategies, you'll likely find me cheering on my favorite sports team, the Razorbacks, while I am embracing my Arkansas roots with a good ol' "Woo Pig Sooie!" 🐗 These days, I call the Dallas-Fort Worth area home, but I'm always down to connect with fellow growth junkies and innovators.

So, if you're looking for a straight shooter who can materialize revenue and drive real results, let's chat!

• The vision for CASA strives to provide a CASA advocate for every abused and neglected child in foster care.
• Previously served on Pulaski County CASA Board of Directors in Little Rock, Arkansas (2018-2020).

Currently building Open Forge AI, a platform of AI agents that helps you grow your brand's visibility on AI search engines and outrank the competition. 

We are a command center for GEO and AEO, where you can measure your AI brand visibility, monitor the competition, and create brand-specific content.

Join my newsletter that teaches you how to get seen by AI Search engines: https://aen.beehiiv.com/subscribe

Subscribe to our YouTube channel to get the latest strategies and tips on getting noticed by AI search engines: https://www.youtube.com/@open_forge_ai

I built and sold an edtech company that provides schools and families with personalized college and career guidance using mentorship, AI, and video instruction. 

Also a Brazilian Jiujitsu Brown Belt.

Jason Patel

The biggest shift in SEO can be summarized in one sentence: 

Your customers won't "search" anymore, at least not like they used to. 

Here's what I mean: Search is moving away from terms like “best calendar for startup founders”

The new search query will become something like:

“Recommend best calendar software for a 150-person Series B company in HR/payroll automation, needs strong security, ample budget for good tool but looking for value, and we need onboarding done in 30 days…also compare the top 3 and tell me what to watch out for.”

It's literally no longer a keyword. Think of it as a purchase path.

In other words, the entire buying journey used to be spread across:

• multiple Google searches
• 10+ tabs
• review sites
• blog posts
• comparison pages
• internal stakeholder debates

More and more, you will see this experience compress through a series of prompts, in one window, in one sitting.

So here’s what I think you should do: Go to your top 5 "money pages" to answer the “context + constraints” version of the question, not the generic one.

When rewriting, I would do this: 

• Speak directly to real buying scenarios (company size, industry, budget, timeline)
• Acknowledge tradeoffs and risks instead of pretending you’re perfect
• Make comparisons easy, explicit, and honest
• Sound like the language your best customers actually use when explaining their problem

What makes this potentially catastrophic to businesses is that if you don’t show up in that compressed funnel, you start at a significant disadvantage. That's because you're literally not in the purchasing conversation. 

But it's also an opportunity. 

Google’s new shopping features prove exactly this: You need to be in that customer pathway. 

That’s where marketing and AI discovery are heading.
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I lead Open Forge AI, a platform of AI agents that helps you outrank your competitors on AI search engines.
 
We are a command center for GEO and AEO, where you can measure your AI brand visibility, monitor the competition, and create brand-specific content.

Co-founder @ Open Forge AI | AI agents that help you rank on AI Search Engines

This post from Elena Dyulgerova struck a nerve with me. 

Almost a decade ago, I was broke as shit, stressed about student loans, and trying to build my first company with more ambition than money. 

I had no idea what I was doing as a founder, and I was funding my learning the only way I could: by putting it on a credit card.
 
I was experimenting with SEO and realized it was one of the first things I touched that showed a clear, compounding return. 

At the time, I was training jiujitsu 5x days a week (my younger self could handle that training load). During the hour-long walks to and from training, I listened to everything I could find on SEO: podcasts, interviews, talks, and a lot of e-books (they were big back in the day). 

I also paid close attention to what people inside Google Search were saying publicly, because I assumed that’s where the real answers would come from.
 
And I quickly realized they were playing a game. 

Google algorithm update after Google algorithm update, they'd say the same things: 

"Create helpful content. Make content readable. Provide value. Be helpful."

They'd say the same thing in a different way, every single time. 

I am now building my second company. Their advice still hasn't changed. 

And I absolutely do NOT blame them.

Elena is right on the money. 

The pros at Google have no incentive or desire to tell you how to do SEO. Their ultimate job is to increase shareholder value and make sure the Google machine continues to print money. 

This is the same for AI SEO, btw. Different side of the same coin. 

The people claiming AI SEO is some completely separate discipline from traditional SEO are overstating it. And the people declaring that SEO is dead are missing the point entirely.

If you’re new to SEO or AI search, you probably shouldn’t be taking tactical advice directly from Google, OpenAI, Anthropic, or any of the platform companies. 

Their job isn’t to help individual businesses figure out how to gain an edge.
 
Instead, you should subscribe to newsletters, podcasts, and LinkedIn posts of the professionals whose job it is to interpret the advice that the Google guys provide.

Track and follow people who are putting their reputations and livelihoods on the line when they give advice. They have skin in the game. 

Here are a few people you should follow: Kevin Indig, Josh Spilker, Lily Ray, Nick LeRoy, Jeremy Moser, Jacky Chou, Despina G., Duane Forrester. There are many other brilliant people in the space.

The real work is taking pieces of what you hear, testing them with your own time and money, and figuring out what works for your specific business. 

No one has a complete map yet. Everyone is learning in public and in real time.

The truth is found through experimentation, iteration, and being willing to be wrong long enough to get it right.

So experiment, fail, learn, and iterate. 

That's what worked in 2017. 

It's what's going to work now.

Prediction: ChatGPT Ads will break the paid ads model. And rewrite the logic of search discovery. It's a danger and an opportunity. 

In Google Ads, companies pay a premium to serve ads that have the potential to drive conversions. "Potential" is the operative word. 
 
ChatGPT Ads will change the logic of search ads: Answers have gravity. They collapse choice and concentrate economic value into fewer slots. 

Simply put, Google Search and Google Ads were different channels in the same interface. ChatGPT Ads won't be another channel because the ads are integrated into the flow of reasoning and intent. 

Sponsored recommendations will appear where they make logical sense inside a conversation, not as visual noise competing for clicks.
 
A sponsored result in ChatGPT won't interrupt the answer. It extends it. Think of it as a native citation with commercial intent. The AI explains a concept, frames the problem, and then suggests a product or service as the next reasonable step. 
 
OpenAI has the chance to make conversational ads feel less like advertising and more like guidance. The danger and the opportunity sit in the same place. Trust becomes the currency: lose it, and the system fails. Earn it, and the economics become very different from anything search marketers are used to managing.
 
This is why the traditional paid search model breaks. It was built for exploration at scale. Conversational AI is built for decision-making. 

Those are not compatible economic systems.
 
3 things you need to action:
 
1. Your cost per click will rise.
 
High-intent commercial (read: when the user wants to buy) queries will cost more because they are no longer diluted by noise. A user asking for a vendor recommendation inside a chat has already self-qualified. They have articulated constraints, budget expectations, and priorities. You will bid on pre-qualified demand, so measure your ACV and LTV before bidding.
 
2. You need to watch out for clutter.
 
If OpenAI treats ads as a "trusted advisor," it can create a true premium experience. That's worth your money. If they do what Google has done these past 5 years and stuff the results page with ads, it's a waste of your money.
 
3. You are fighting invisibility.
 
Google's ten blue links model thrives on abundance. Multiple advertisers can coexist, so arbitrage lives in the gaps. Low-intent clicks also subsidize the system. Conversational AI destroys that abundance by design. It is optimized to provide the best answer, not many answers.
 
This scarcity changes everything. When there is one primary recommendation instead of several ads, bidding dynamics invert. You are no longer competing for position three or four; you are instead competing to be the source of truth. And show up.
 
Here is my recommendation: Experiment early. Learn while costs are unstable and competition is still forming opinions. We are transforming ads from a consideration factor to a decision-making choke point.

Reach out if this resonates.

AI search is patriotic. It’s good for America. 

It’s a cheesy thing to say. Bear with me.

This is NOT a “technology will save us all” argument. Most of the time, when founders talk like that, it’s pure hype aka big claims with soft substance and deserved eye-rolls.

I’m talking about something much more boring and much more real: economics.

The American economy has always been strongest when more people can build, compete, and win on merit. AI search / GEO nudges us back in that direction.

Read my article below and tell me what you think.

Let me clear the air first. It is genuinely annoying when entrepreneurs say their technology is “going to change the world.” Most of the time, it is e

Open article: The Patriotic Case for AI Search: How GEO Empowers the American Small Business by Jason Patel • 6 min read

The Patriotic Case for AI Search: How GEO Empowers the American Small Business

Prediction: We're thinking about ChatGPT ads the wrong way. Here's how you'll need to think about brand vs performance budgets. ⏬

You first need to rethink how you view ads in LLMs. Think of this as a physics transformation, not a channel change. 

Since the distance between awareness and action is collapsing, the rules that depended on it will also fail. This includes failures in last-click attribution, funnel-stage separation, and channel-based budgeting.

Therefore, the right budgeting question is no longer which channel deserves more spend. The correct question is whether your brand is selectable in the answer at all, and whether you can win once you are considered. 

That reframing leads directly to a new equation:

Eligibility vs. Capture Spend

"Eligibility Spend" is the investment required for a model like ChatGPT to credibly include your brand in an answer. 

It answers one question: Can the model trust you enough to mention you without embarrassing itself (aka without the user trying out Gemini). 

Eligibility Spend includes:
 
• Clear category positioning
• Mid- and bottom-funnel content that supports decision-making
• Third-party proof, such as reviews, industry mentions, and citations
• Consistency across the web so the model can trust you
 
If eligibility is low, ads won't help, just like pouring lighter fluid on a dead campfire. The model cannot convincingly place you in the answer, sponsored or not, if your eligibility isn't optimized. 

"Capture Spend" is the investment used to win the recommendation once eligibility exists. This is where ChatGPT Ads will live. 

Capture Spend answers a different question: When the model already understands you, can you reinforce the decision in your favor? 

Straight up, I think the best way to budget is to focus 70-90% of your budget on Eligibility Spend and 10-30% on Capture Spend. 

I think this split works because:
 
• Eligibility compounds over time and lowers future ad costs because you are building influence and brand power. 
• Capture without Eligibility feels forced and will underperform.
• OpenAI will probably play it safe, with early ChatGPT Ads inventory rewardubg trusted brands disproportionately to keep users from going to Gemini. 

If you skip Eligibility and jump straight to Capture, the result will be quiet cash burn. The ads will  run, but the model will not lean toward them. And you won't know your real bottlenecks in the LLM. 

If all else fails, just remember: 

Google Ads sell clicks. ChatGPT Ads will sell placement inside reasoning.
 
Google Ads interrupt the experience. On ChatGPT, they'll extend it. The sponsored recommendation appears where it logically fits within the answer. If it's done well, it feels additive rather than promotional.

With ChatGPT ads, you will not be playing a demand creation game. You will play a conversion game.  

This is Decision-Layer Amplification, 

More on that later.
 
What do you think? Agree or disagree?

You need to stop copying what your friends are doing in GEO / AEO. What works for them won't work for you.

ChatGPT doesn’t use the same signals across industries.

I went on 🎤John H. Corcoran (B2B Podcasting Expert) ‘s Smart Business podcast to discuss the biggest trends in GEO / AEO. I also covered the worst mistakes I've seen companies make.

ChatGPT behaves less like a search engine and more like a probability engine. Its job is to predict the most reliable answer given context, risk, and prior knowledge. That means the signals it values are not universal.
 
In low-risk categories like consumer retail, the model can tolerate lighter evidence. In high-risk categories like finance, health, or legal services, the bar is much higher. This mirrors Google’s YMYL concept, but enforced more aggressively. 

If advice can materially affect money, safety, or health, the model demands stronger proof.
 
A B2B SaaS company and a local contractor are evaluated using entirely different trust scaffolds. For SaaS, the model looks for signals like detailed documentation, customer reviews on platforms like G2, integration references, and technical depth. For a local contractor, it looks for geographic consistency, local reviews, neighborhood mentions, and evidence of real-world activity in a specific area.
 
So why should you care?

Trust scaffolding is the external validation layer AI systems use to confirm what they already suspect. It is built from third-party mentions, citations, reviews, and references that exist outside your owned properties.
 
These trust nodes vary by industry. 

• In SaaS, they include review platforms, technical forums, and analyst coverage. 
• In healthcare, they include clinical sources and regulatory bodies. 
• In local services, they include review ecosystems and regional publications.

And so one of the fastest ways to identify respected sources is to study AI outputs themselves. Ask the model questions about your niche and see where your competitors appear. Observe which sites are cited repeatedly. Those citations reveal the scaffolding the model already trusts.
 
BTW, this is also why your PR strategy must change. Precision placements in industry-specific outlets will matter far more than scale. Think roundups and micro-influencers.

Moral of the story: Figure out the trust factors that influence AI answers for your particular industry. 

Your buddy who runs a successful car dealership will have different trust scaffolding than your cousin who has a skincare brand. 

Interview link in comments.

DM if you need help.

Everything you do in GEO / AEO should be focused on one thing: building your "Category Authority." It's why I think the structure of marketing teams will change.  

When talking to marketers, I make the skyscraper analogy:

Every building block of category authority you place on your brand helps your skyscraper tower above the clouds. 

If you don't have enough of those building blocks, your LLM visibility will be trapped in a sea of generic content. That's OK for early-stage startups but a huge missed opportunity for more mature companies. 

And the key building block for category authority is third-party citations. I see this with 80%+ of the companies I work with; content gets you in the game, but third parties get you across the goal line. 

This is where marketing teams will change: 

Third-party citations cannot be treated as a byproduct of marketing. They will be treated as an operation.

Most teams approach citations passively because that's what worked on Google. They waited to be discovered, mentioned, or reviewed. That approach does not scale, and it leaves authority formation to chance.

The first step is understanding where AI systems source information in your category. You don't need an enterprise-grade GEO / AEO product. 

You can just reverse-engineer directly from customer-style prompts: Ask the same questions your buyers ask, such as “best payroll software for mid-sized fintech companies” or “top SOC 2 compliance tools for SaaS.” Then observe which sources are cited. 

Do this over and over again. 
 
Patterns will emerge quickly. Industry roundups, listicles, review platforms, and niche publications tend to dominate. See which ones dominate your niche. 

But before aggressively scaling third-party citations, you need to lock down your product positioning. Don't skip this step, or risk long-term problems as you scale your LLM visibility. 
 
AI systems learn from repetition. They are machines, after all. If your brand is described inconsistently across sources, the model will struggle to form a stable association. 

You may show up in answers, but in the wrong context or for the wrong buyer. That's hard work and time down the drain.

Clear positioning answers a few critical questions:
 
• Who is this product for?
• Who is it not for?
• What problems does it solve best?
• Where does it lose to alternatives?

And those answers need to be consistent everywhere. Review sites, roundups, analyst commentary, and editorial mentions should reinforce the same narrative. When this happens, citations compound not just in volume, but in semantic alignment.

Over time, the model will stop hedging. It'll confidently place your brand in the correct decision set, which will increase your conversion rates.

This is not easy work. Third-party citations take longer to secure than publishing a blog post. But they carry more weight, and they stack over time.

But it'll be worth it. 

What do you think? Right or wrong?

Are you struggling to decide whether GEO/AEO is worth the effort? Try this framework. 

You have a binary choice in AI search: Attack or Defend. 

AI search is removing the luxury of gradualism. Buyers are moving away from the spectrum of visibility that runs from page one to page ten, even when no one scrolled past page one. 

There is only inclusion or exclusion. For AI buyers, absence is not a softer form of underperformance. There is no passive lane.

That means indecision in this environment is not neutral. Choosing to wait, barely experiment, or see how things shake out is functionally a decision to allow the model to ignore you. AI search is going to make your judgments on your company, whether you like it or not.

If your company is at a certain size, opting out is impossible. This leads to a distinction between intentional definition and incidental definition.

• Intentional definition occurs when a company actively shapes how it is described, categorized, and compared. The AI learns what you do, who you are for, and why you exist through consistent, structured signals.
• Incidental definition occurs when the AI is forced to infer your positioning from fragments. These fragments might include outdated articles, competitor comparisons, partial mentions, or adjacent category references. 

When a brand leaves gaps, the AI will not wait. It'll guess. And that guest will not be generous to you. 

The long-term cost of letting AI infer your positioning is that correcting a bad narrative requires significantly more effort than establishing a good one early. You'll need to add information and un-teach the model.
 
Therefore, the defend-versus-attack decision is not philosophical. It is situational. 

Companies that must attack include those with brand recognition but weak (and real) market share. Think $1M ARR+. 

• These brands are known but not chosen. 
• Also challenger brands consistently excluded from AI answers. 
 
Attack strategies are about breaking consensus. Attackers must force the model to acknowledge them as viable options.

Companies that must defend include:

• Enterprise brands
• Category leaders
• Companies already appearing in AI answers 
• Smaller teams with unusually strong visibility carried over from legacy SEO success
 
Defense strategies are about preservation. Defenders must protect their narrative from erosion, oversimplification, or displacement by challengers. 

Both strategies are active. 

But don't make the assumption that size equals safety. It does not. I've seen GEO/AEO strategies work in 7 days. Good traditional SEO took 7 months.

This speed differential creates vulnerability for large brands. Challengers will catch up faster than incumbents expect. 

Being big doesn't protect you like it used to.

What do you think?

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The Battle

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