SEO still matters. GEO matters too. Modern content strategy requires both.

Buyers no longer rely only on traditional search engines to research new investments and solutions. A meaningful and growing percentage now use large language models (LLMs) to compare products, shortlist vendors, and define buying criteria. Those buyers often focus on vendors surfaced by LLMs, and do not search beyond those initial lists. 

This shift toward zero -click research makes it increasingly difficult to measure performance using traditional traffic metrics alone. MGC helps clients quantify this shift beyond traditional traffic metrics. We interview clients’ buyers and ask those customers how they research our clients’ categories, estimate what percentage are using LLMs now, and scenario-plan for future LLM adoption. Whether 10%, 20%, or 50% percent of the market is researching through generative systems today, we build scalable solutions that consider the current situation and future growth. 

GEO tactics are different from traditional SEO techniques. Most GEO consultants are repackaging SEO as GEO. If they aren’t recommending analytic solutions to see how LLMs source relevant knowledge sources about your brand, category, and competitors, they aren’t providing the roadmap for LLM success.

How do you ensure AI-generated content sounds like our CEO?

To create MGC’s new website, we leveraged the most modern approaches and sourced suggestions from top industry experts. We started our process by defining our messaging with a content strategist. She interviewed Mark Goloboy, asking for his experience, MGC history, our consulting and staffing approach, and how we assign top level talent to meet client needs. The content strategist then developed the new website content based on the interviews. From there, Mark Goloboy listed the areas that clients are most likely to ask about. Mark recorded detailed conversations about each area as LLM prompts. He then edited the GPTs descriptive summaries and published them as GPT training. 

MGC proved this model was feasible with a client version of this CEO training that proved the model in 2024. A CEO insisted that AI could never match her technical expertise in writing. She is a PhD behavioral psychologist with a sophisticated communication style whose content is directed to other PhDs. We trained a custom GPT on her interviews, published articles, webinars, and long-form thought leadership. 

To test the quality:

1. She wrote a set of sample posts.
2. Our model wrote the same topics.
3. She could not tell which ones were hers.

She took hours. AI took seconds.

The writing models have improved dramatically since then. As have the methodologies for voice prompting. With the right training data, we can match tone, depth, structure, and nuance in ways that are indistinguishable from human writing while maintaining accuracy and brand standards.

What is an AI content factory?

Most organizations rely on human writing while experimenting with AI in isolated ways. An AI content factory operationalizes AI at production scale.

A mature AI content factory includes:

• Brand and voice guidelines encoded directly into writing systems
• Structured workflows for AI generation and human-in-the-loop review
• Content broken into smaller, atomic elements
• Automated change detection to limit review volume
  Translation and localization applied only to updated elements
• Scalable publishing across CMSs and regions

In a real client example, a public company had 200 product specifications documents which were updated as part of monthly release cycles. MGC focused only on the product marketing sections to eliminate AI intellectual property leakage risk from other internal sections of the document. MGC designed a solution based on a composable CMS to manage the workflow and detect the updated sections. We then selected a writing tool that regenerated only the affected web content, translated it into seven languages, routed it for review, and published pages reliably every month.

Once the system was built, the final step would be integrating SEO, GEO, and Web Testing to create a feedback loop that improved the content production each month. AI content factories improve SEO and GEO,  reduce effort, improve quality, and give teams the capacity to support scalable global growth.

Should we reallocate our Google ad budget to GEO?

Google Ads are often the largest marketing expense. They are also the easiest spend for CFOs to cut. This gives marketing leaders an opportunity to reallocate a portion of that spend toward GEO pilots without requesting incremental budget. This meets the CFO’s desire for innovative new programs that drive pipeline and revenue growth, without incremental marketing budget requests. 

We typically recommend clients:

• Analyze LLM sources to find new media opportunities for paid and develop new organic campaigns
• Use a controlled portion of SEM budget to fund GEO paid experiments
• Measure whether generative influence drives new buyer research and improved funnel conversion
• Reduce dependence on paid tactics by investing in long-term optimization

When companies compare SEM results to GEO results, many find that GEO creates more durable exposure and higher quality intent. GEO does not replace SEM, but reallocating the budget is often the best way to fund innovation.

How does MGC manage the risk of AI hallucinations?

We never treat AI published content as finished work. Every piece of content, whether created by AI or a junior writer, is reviewed by a senior, experienced marketer who understands the product, messaging, data accuracy requirements, and brand standards.

Our human-in-the-loop model ensures:

• No unverified claims
• No invented product capabilities
• No misaligned messaging
• No publishing risk

AI accelerates creation. Humans guarantee quality.

Can you automate my agency’s operations with AI?

Yes. Large, recurring agency investments should be reviewed for AI-driven efficiencies especially when work is expensive or difficult to scale.

Our approach:

• Brainstorm with agency executives to identify high-cost, high-effort agency activities
• Prioritize AI solutions based on Value, Cost, and Risk
• Measure activity cost versus AI-assisted execution

This often reveals:

• Lower-cost ways to achieve the same outcomes
• Faster production cycles
• Better transparency and control
• Reduced long-term dependency on external teams

AI gives clients resource leverage to scale existing operations to more clients without incremental hiring. The goal is not to eliminate agency resources, but to modernize how work gets done.