AI Search Visibility: Peec Doubles Its $10M Metrics in Just a Few Months

The history of quiet but significant growth

Peec was born in Berlin with a seemingly niche idea: to measure how often—and in what way—brands are mentioned in artificial intelligence-generated responses. However, the market has responded surprisingly. According to sources cited by TechCrunch, the startup has more than doubled its annualized revenue in a few months, surpassing the $10 million mark.

This growth rate isn't random. In fact, it coincides with the rapid spread of tools like ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot. These systems no longer return a list of links; they generate direct answers, selectively citing sources and brands. Consequently, a company's visibility no longer depends solely on its Google ranking, but also on how often language models mention it.

Peec intercepted this need before it became mainstream. Therefore, its trajectory tells something bigger than a single startup: it tells of a structural change in how consumers and B2B buyers seek information.

Winners and losers in the new AI search ecosystem

Who benefits from the growth of Peec? First and foremost, brands that have already invested in authoritative, structured, and citable content. Language models tend to favor sources that appear reliable, up-to-date, and well-organized. Therefore, companies with a strong editorial presence—blogs, white papers, detailed product sheets—have an advantage.

On the contrary, SMEs that have neglected digital content production find themselves in a critical position. Not because they are actively penalized, but because they simply do not exist in the body of information that AI models use to construct their answers. This is a problem of invisibility, not of penalty.

Among the potential losers are SEO agencies and professionals who continue to measure success solely through traditional metrics: Google rankings, organic traffic, and CTR. These metrics remain relevant, but they are insufficient. Just as with the shift from desktop to mobile, those who fail to update their measurement tools risk optimizing for a channel that is becoming increasingly less important.

Finally, winners also include platforms that—like Peec—offer dedicated AI search monitoring tools. The fact that a European startup has reached $10 million in ARR in this segment suggests that demand is real and rapidly growing.

SHM Studio Reading: What's Really Changing

We of SHM Studio We have been observing this phenomenon carefully for several months. Peec's growth confirms a thesis we have long supported: optimization for AI search—often referred to Answer Engine Optimization o AEO is not a fad. It is an emerging discipline with solid foundations.

However, it's important not to panic or overreact. Traditional SEO is not dead. Furthermore, Google remains the primary entry point for most commercial searches in Italy. The issue isn't replacing one strategy with another, but integrating them cohesively.

What is changing is the logic of visibility. In the past, it was enough to appear on the first page of search results. Today, it's also necessary to be cited in AI-generated answers. Therefore, the quality of content – its depth, structure, and authority – becomes even more crucial. Language models do not cite keyword-optimized pages: they cite sources that comprehensively answer specific questions.

According to research from Gartner, the volume of traditional searches could significantly decrease by 2027-2028 due to the spread of AI chatbots. This data should not be interpreted as a catastrophe, but as a signal of user attention reallocation.

The construction site is still open: what is missing from current tools

Peec's growth is impressive, but the AI search monitoring market is still under construction. Therefore, it's useful to understand what these tools can do today and what they still can't accurately measure.

Currently, monitoring platforms like Peec track the frequency of brand mentions in AI responses, the associated sentiment, and competitors mentioned for comparison. These are useful metrics, but they are incomplete. In fact, there is still no agreed-upon standard for measuring the impact of AI mentions on actual conversions. The link between visibility in AI search and revenue remains difficult to quantify.

In addition to this, language models update their knowledge corpus in a non-transparent way. Therefore, a brand could be well-positioned today and lose visibility tomorrow without receiving any explicit signals. This opacity is one of the main challenges the sector still needs to solve.

As highlighted by Harvard Business Review, trust in AI-generated content largely depends on the quality of the underlying sources. For this reason, investing in authoritative content is the most robust long-term strategy, regardless of the monitoring tools used.

Operational Implications for Italian B2B and Retail SMEs

What specific steps should an Italian SME take in the face of these changes? The answer isn’t to immediately purchase an AI search monitoring tool. First and foremost, it’s essential to lay the right foundation.

The first step is an audit of existing content. Do the website pages provide comprehensive answers to the questions buyers typically ask? Are they structured in a way that allows a language model to extract clear information from them? If the answer is no, the problem isn’t the lack of a monitoring tool—it’s the lack of quotable content. Our team of SEO copywriting It works exactly at this level.

Subsequently, it is advisable to strengthen the presence on sources that AI models tend to favor: Wikipedia, authoritative industry websites, indexed press releases, and mentions in recognized digital media. This process is often referred to digital PR and integrates seamlessly with a strategy SEO well-structured.

Furthermore, for B2B companies, LinkedIn represents an increasingly relevant channel. Some AI models draw on public LinkedIn content to build responses about companies and professionals. Therefore, an active and consistent presence on the platform—through LinkedIn campaign organic content — indirectly contributes to visibility in AI search.

Finally, for retail SMEs, the product page remains the fundamental unit. Detailed descriptions, complete technical specifications, verified reviews: these are all elements that language models use to build responses to purchase queries. A website web optimized in this direction is the basis of any future visibility strategy.

Next moves: three concrete priorities for the second half of 2026

The growth of Peec indicates that the market is moving rapidly. However, there is no need — nor is it advisable — to react with disorganized urgency. There are three priorities that Italian SMEs can address in a structured manner in the coming months.

The top priority is to identify AI queries relevant to your industry. What questions do potential customers ask ChatGPT or Perplexity before making a purchase? How does the AI respond to these questions? Does the brand appear in the answers? This analysis can be conducted even without specialized tools, simply by manually testing the most common queries. The team at AI strategy di SHM Studio supports companies in this exploratory phase.

The second priority is structured content production. In-depth articles, detailed FAQs, industry guides: these are the formats that AI models cite most frequently. A strategy of digital marketing Integrating these formats increases both traditional organic traffic and visibility in AI search.

The third priority is progressive monitoring. It's not necessary to adopt a tool like Peec right away. However, it's useful to start manually tracking mentions of your brand in AI responses, at least on a monthly basis. This creates a data baseline that will be invaluable when you decide to adopt more sophisticated tools.

To delve deeper into these topics or request an assessment of your digital presence, you can Contact the SHM Studio team. Additionally, further resources and updates are available on our blog. Le Google Ads campaigns remain an effective complementary tool for maintaining visibility during the transition to new research paradigms.