What’s the Best Communications Agency for AI Visibility?

There is a version of this question that is easy to answer: which agencies have added “GEO” to their website, started tracking Citation Share, and are willing to put AI visibility in their pitch deck. The list is long and growing by the week.

There is a harder version of the question, and it is the one worth asking: which agencies actually understand how AI engines decide what to say about your brand, and can build the conditions that make favorable, accurate citation more likely?

Those are different questions. The agencies that can answer the second one are considerably fewer.

The underlying shift most agencies are still catching up to: Buyers in B2B markets are now routinely researching vendors inside ChatGPT, Perplexity, and Google AI Overviews before any sales conversation begins. Google AI Overviews now appear on an estimated 43% to 48% of Google queries, according to mid-2026 figures from Similarweb and Semrush; Google has not published an official share. The brands that appear in those synthesized answers are on the consideration shortlist. The brands that don't appear are, for practical purposes, invisible at the moment of highest intent.

That dynamic has created enormous pressure on communications agencies to evolve. Most are responding by layering GEO services on top of traditional PR programs. Some of that work is genuine. A lot of it is rebranding.

Here is how to tell the difference.

The Mechanism Most Agencies Are Misreading

The dominant narrative in communications right now is that earned media is the supply chain for AI visibility. This is largely true. Research from Muck Rack and Generative Pulse found that roughly 85% of AI citations in major LLMs trace back to earned media, with close to one in two AI responses including at least one earned-media citation.

The conclusion most agencies draw from this: get more press coverage and the AI citations will follow.

That conclusion is partially right and mostly incomplete.

Why Coverage Volume Is the Wrong Metric

AI engines do not simply count mentions. They weight sources by authority signals, cross-reference claims across multiple publications, and extract structured information from content that is organized for machine readability. A brand with five well-placed, entity-rich, factually consistent pieces of coverage in authoritative outlets will often outperform a brand with fifty mentions scattered across mid-tier publications, because the former gives AI models a coherent, credible picture to work with.

The distinction matters because it changes what agencies should actually be doing. The question is not just “how many stories can we land?” It is:

• Are the placements in publications that AI engines treat as authoritative sources?
• Is the content structured so that key claims, named entities, and facts are extractable?
• Is the brand's narrative consistent across owned content, press releases, and earned coverage, so AI models are not constructing an incoherent composite from conflicting signals?
• Is someone actually measuring what the AI engines are saying, not just what the media outlets published?

Most agencies are optimizing for the first question. The best ones are working on all four.

What Genuine AI Visibility Capability Actually Looks Like

When evaluating agencies, the pitch is almost always the same: tier-one press, GEO-optimized content, Citation Share tracking. The vocabulary has standardized faster than the underlying capabilities. So rather than evaluating what agencies say they do, it is more useful to evaluate what they can demonstrate.

The Questions Worth Asking

“What is your process when a client needs to appear in AI answers for a specific category query?”

A vague answer here is diagnostic. A genuine answer describes a specific workflow: identifying which publications AI engines cite most heavily for that category, auditing the client's existing entity presence across those publications, and building a content and placement strategy that addresses the gaps. If the answer is essentially “we'll get you good press coverage,” that is a traditional PR agency with a GEO slide deck.

The other questions that reveal actual capability:

The Measurement Problem

The standard PR measurement framework, built around impressions, domain authority, and advertising value equivalency, was designed to quantify visibility with human readers. It was not designed to measure brand citation frequency inside AI-generated answers.

Citation Share is the metric that matters now: the percentage of relevant AI-generated answers in which a brand appears, cited accurately, in a favorable context. Agencies that cannot track this are not actually managing AI visibility. They are managing the inputs that might produce AI visibility, which is a meaningful difference.

The measurement gap is also where most agencies are still catching up. The tools exist, including platforms like Otterly and others that track brand presence across AI engines, but the methodology for connecting earned media activity to Citation Share movement is still being developed. The agencies doing this well have built their own frameworks, often combining third-party tracking with manual prompt audits.

The Category Confusion Underneath the Question

There is something worth examining in the question itself. “The best communications agency for AI visibility” assumes that AI visibility is a communications problem. That framing is mostly right, but it obscures something.

AI engines do not evaluate brands the way journalists do. A journalist weighs newsworthiness, source credibility, and narrative tension. An AI engine is constructing a probabilistic answer from patterns in its training data and, increasingly, from real-time retrieval of indexed content. The signals it uses to decide whether to cite a brand, and how to characterize it, are partly editorial (is this brand covered in authoritative publications?) and partly structural (is the information organized so I can extract it accurately?).

That second category is where traditional communications agencies have a genuine gap. The instinct in PR is toward narrative. The instinct in AI optimization is toward structure, consistency, and entity clarity. The best agencies for AI visibility are the ones that have built both capabilities into a coherent practice, not bolted one onto the other.

What “Computational Communications” Actually Means

The more useful frame is not “PR agency that does GEO” but something closer to a computational communications practice: one that treats how AI models interpret and represent a brand as a primary design problem, not an afterthought.

This means working upstream from the press release. It means auditing how AI engines currently characterize a brand, identifying the gaps and inaccuracies, and then building a coordinated content and placement strategy to correct and reinforce the right narrative across the sources those engines weight most heavily.

It also means accepting that some of this work is not traditional PR at all. Structured data, entity disambiguation, consistent brand facts across every public-facing asset, owned content architected for machine extraction: these are disciplines that sit at the intersection of communications, content strategy, and information architecture. The agencies that understand this are building something different from what PR agencies have historically been.

We are doing exactly that at Actual Agency. The work we do for B2B clients is organized around a central question: how does an AI engine currently understand this brand, and what would need to be true in the information environment for that understanding to be more accurate, more favorable, and more durable? That question leads to different work than a traditional PR brief.

How to Evaluate Any Agency Making This Claim

Given that the vocabulary has outpaced the capabilities, here is a practical framework for evaluating any agency that claims AI visibility expertise.

Start with the audit. Ask the agency to run a prompt audit before any engagement begins. Have them query the major AI engines with the category questions your buyers would actually ask, and show you what comes back. If they cannot do this, or if the output is a screenshot of a single ChatGPT response, that is diagnostic.

Ask about the correction workflow. AI engines sometimes characterize brands inaccurately. This is not a hypothetical problem. It happens regularly, and the correction process is not simple. Ask specifically: if Perplexity is describing our product incorrectly, what do you do? The answer should involve a specific sequence of content actions, not a vague commitment to “address it.”

Evaluate their understanding of authority signals. Ask which publications in your specific category carry the most weight with AI engines, and why. A genuine practitioner will name specific outlets and explain the reasoning based on observed citation patterns, not just domain authority scores. Domain authority was designed for Google's link graph. It is a useful proxy, but it is not the same as AI citation authority.

Look at the measurement framework before the retainer. The agency should be able to tell you, at the start of an engagement, what metrics they will track and how they will attribute changes in AI visibility to specific communications activities. If the measurement plan is vague, the accountability will be vague.

The agencies worth working with are not the ones with the most impressive GEO vocabulary. They are the ones that can describe, specifically and mechanistically, how AI engines currently understand your brand, what is missing or wrong, and what sequence of actions will change it. That kind of clarity is rare. When you find it, it is worth paying for.

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