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Pragmatica on GEO: From Traffic to AI-Driven Visibility

As generative AI becomes a core layer in how users access information, agencies are starting to rethink one of the most fundamental questions in digital marketing: what does it actually mean to be visible?

In this edition of The GEO Series, Alex Alokhin, SEO Expert at Pragmatica, shares a perspective rooted in performance but evolving toward something broader.

Based in Vancouver, Canada, Alex and his team treat GEO as a complementary capability, yet one that is increasingly shaping how visibility is measured and understood.

GEO Strategy & Services

At Pragmatica, GEO is not positioned as a standalone product but as an extension of existing services.

Alex Alokhin and his team integrate GEO across:

• Content
• Digital PR
• Performance frameworks

This reflects a more pragmatic approach to adoption.

Rather than reinventing their offering, Pragmatica is layering GEO into what already works while gradually expanding its role.

At the same time, client demand is already visible. Most clients are starting to ask about GEO, even if the level of understanding varies.

This reinforces a pattern seen across The GEO Series: “demand is rising, but education is still a key part of the process”.

How Pragmatica Structures GEO Services

Pragmatica builds its GEO approach around ongoing observation and refinement.

Their process typically involves:

• Tracking brand presence across a range of AI environments
• Examining how that presence changes over time
• Identifying gaps or inconsistencies in how the brand is represented
• Feeding these insights back into content and strategy decisions

This creates a feedback-driven model.

Instead of a fixed optimization phase, GEO becomes an iterative process, shaped by how AI systems continue to interpret and surface information.

Over time, this allows the agency to build a clearer picture of how a brand is understood and where adjustments are needed.

How GEO Actually Works

Alex Alokhin summarizes the shift in a way that appears repeatedly across conversations:

This distinction is central to understanding GEO. Traditional search engines rank pages to drive clicks. Generative AI systems, however, synthezise information prioritising consensus, credibility, and semantic relationships.

For Pragmatica, this means that success is no longer about ranking higher, but about becoming a trusted source within AI-generated responses.

This places particular importance on first-hand expertise and original insight.

Measuring GEO Performance

Measurement is where Alex and the team at Pragmatica take a more structured and granular approach.

Their framework includes:

• AI Share of Voice
• Citation velocity
• Sentiment in AI-generated summaries
• Hallucination rate
• Prompt-to-site click-through rates

Together, these metrics move beyond traditional traffic signals and focus on how brands are interpreted, represented, and surfaced within AI-generated answers.

Looking at the broader dataset, this approach aligns closely with how the industry is evolving.

AI Share of Voice emerges as the leading KPI followed by metrics tied to traffic quality and conversion influence.

Meanwhile, cross-platform mention coverage and brand positioning signals highlight a growing focus on presence and consistency across AI environments.

Alex’s framework fits directly within this shift but goes a step further.

By incorporating metrics like citation velocity and hallucination tracking, Pragmatica moves toward a more diagnostic model, one that not only tracks visibility, but also evaluates accuracy and trustworthiness.

However, even with this level of sophistication, one challenge remains consistent: measurement and attribution.

While agencies are getting better at tracking visibility within AI systems, connecting that visibility back to business impact is still evolving.

Generative Engine Optimization Tools & Platforms

For Alex Alokhin and the team at Pragmatica, GEO is closely tied to where AI-driven discovery actually happens.

Today, ChatGPT, Google AI Overviews, and Gemini stand out as the most critical platforms as they’re reflecting a shift from search engines to answer engines.

But unlike traditional search, these platforms operate with different levels of transparency and citation logic, making GEO harder to standardize.

To navigate this, Pragmatica relies on a hybrid stack:

• Google Search Console and Google Analytics for baseline signals
• Ahrefs for authority insights
• Ahrefs ������ýŮ�� Radar for tracking AI-driven visibility

This highlights a key industry reality:

GEO still depends on adapting existing tools rather than purpose-built solutions.

And this leads to the biggest limitation, such as unreliable AI citation tracking.

Without consistent visibility into how AI systems select sources, optimization remains partially blind.

The Future of GEO

Pragmatica sees GEO as a natural continuation of how search is evolving.

“A natural evolution of SEO within existing structures.”

Yet this evolution brings a shift in how performance is understood.

Rather than focusing solely on traffic and rankings, agencies will increasingly need to measure presence, influence, and visibility within AI-generated responses.

As generative AI becomes more integrated into everyday search experiences, GEO will likely redefine what it means to be “v������������” online.

In this context, success will depend not only on being found but on being interpreted, trusted, and surfaced by AI systems.