If I walk into a board meeting and show a slide titled "AI Visibility," I’m getting fired—or at the very least, laughed out of the room. "Visibility" is a vanity metric, a placeholder for people who don’t have real data. As an SEO and analytics lead who has spent nearly a decade architecting attribution models for multi-market brands, I don’t care about "visibility." I care about Share of Voice (SoV), Citation Frequency, and Attributed Conversion Volume.
When you operate across multiple markets—say, Germany, Japan, and the U.S.—the challenge of tracking AI search isn't just about the language. It’s about the underlying models, the localized prompt databases, and whether your tool can actually tell you which LLM is serving your brand content. If you aren't integrating your AI tracking data into your existing GA4 integration or Adobe Analytics integration pipelines, you aren't doing search; you’re just guessing.
What Would I Show in a Weekly Report?
Before we look at the tools, let’s define the output. If a vendor asks me to pay for an "AI SEO" platform, my first question is: What would I show in a weekly report?
I don’t want "growth charts." I want to see:
- Engine-Specific Citation Share: What percentage of answers in ChatGPT (GPT-4o), Claude 3.5, and Perplexity include our brand as a primary entity? Multi-language Prompt Performance: When a user in the local language enters a high-intent query, does our brand appear in the "featured" answer or the "long-tail" citations? Direct Correlation to Conversion: Does the uptick in AI citations correlate with direct or organic traffic spikes in our GA4/Adobe Analytics dashboard?
If a tool cannot map its internal database of results to a reportable export that feeds into my BI stack, it is useless to me. Let’s look at how the current landscape of multi-language AI visibility tools stacks up.
The Players: Semrush, Peec AI, and Otterly AI
We are currently seeing a split in the market. On one hand, you have the legacy players trying to retrofit their massive datasets; on the other, you have the agile, AI-native startups.
Semrush
Semrush is the behemoth. Their strength lies in their massive historical database of keyword rankings. When you look at their AI search capabilities, you are seeing a company that is trying to bridge the gap between traditional SERPs and AI answers. However, their depth in LLM-specific nuance—specifically regarding how different languages influence model behavior—is still catching up to smaller, specialized competitors.
Peec AI
Peec AI has staked its reputation on multi-language AI visibility. What draws me to them is their handling of multi-country prompts. In a multi-market brand environment, you cannot simply translate a prompt and expect the same search behavior. You need to test prompts that are linguistically and culturally native. Peec AI’s database depth allows for localized testing that mirrors actual user behavior in non-English territories, which is critical for global enterprise brands.
Otterly AI
Otterly AI positions itself around the specific surface area of AI search—the "answers" rather than just the rankings. They are lean and focused on the output structure of major LLMs. For brands that are obsessed with the "hallucination vs. citation" ratio, Otterly provides a unique view into whether your brand is being pulled into the core answer or merely mentioned in the sidebar.
Engine Coverage: The Table of Truth
I keep a running list of engines that every tool I use covers. If it isn't listed, it isn't being tracked. Here is how these platforms compare based on publicly available data sources:
Feature/Engine Semrush Peec AI Otterly AI ChatGPT (GPT-4o) Partial Deep Deep Perplexity AI Minimal Deep Deep Google Gemini (AIO) Strong Developing Developing Claude 3.5 No Yes Yes Localized/Country-Specific Models Limited Extensive Moderate GA4/Adobe Integration Native API-first Webhook/APIAddressing the Multi-Language Challenge
The primary pitfall I see in global SEO is the "One Prompt to Rule Them All" strategy. You cannot verify multi-language AI visibility by using English-language seeds. You need a platform that manages a localized prompt database.
Peec AI’s multi-language capabilities allow us to segment our reporting by region. If I’m looking at our France operations, I need to know how our brand is cited in French-language responses generated by models tuned for European data sets. A tool that only scrapes English-language results and translates them is not providing intelligence—it’s providing noise. If a tool doesn't specify how many hundreds of thousands of localized, multi-country prompts they run daily, how are they training their visibility scoring?
Brand Mentions vs. Citations vs. Share of Voice
This is where most managers get confused. We need to distinguish between three distinct metrics:
Brand Mentions: The LLM is "aware" of you. It can spit out your name. This is low-value. Citations: Your URL is included as a source of truth for the answer. This is medium-value. Share of Voice (SoV): The LLM positions you as the preferred expert or product choice for a high-intent, long-tail query. This is high-value and directly correlates to revenue.When choosing a tool, don't look for "Brand Mentions." Look for "Citation Attribution." If the tool can’t report on the source URL that the LLM pulled, you cannot perform any meaningful GA4 integration to see if that citation actually drove a qualified lead.
The Integration Gap: Why Data Source Matters
I don't care how "pretty" the dashboard is if the data isn't clean. When evaluating these tools, I always ask about their update cadence. AI search is shifting daily. If a tool updates its "visibility" metrics once a month, it is useless for any enterprise brand. You need, at minimum, weekly data ingestion.
Furthermore, look for tools that offer robust API support for Adobe Analytics integration. If you are a large organization, you aren't going to log into a third-party dashboard to check your rankings. You are going to pull that data into your own Data Warehouse or BI Go to the website tool (like Tableau or Looker). If the tool doesn't have a clean, documented API, they are creating a data silo.
Final Verdict: Which Tool to Choose?
Choosing the "best" tool depends entirely on your current maturity level:
- For the Enterprise Global Brand: Peec AI is the strongest contender for multi-language AI visibility. Their focus on the prompt database and localized search behavior makes them the clear choice if you are managing complex, multi-market SEO efforts. For the Surface-Level Optimizer: Otterly AI provides excellent granularity into the "AI answer" environment. They are perfect for brands that want to see exactly how they appear in the new generation of LLM search surfaces. For the Legacy SEO Team: Semrush remains the king of the "everything else" category. If you are already deeply embedded in their ecosystem and need to track traditional SERPs alongside emerging AI trends, they offer the most convenient (though perhaps less AI-specialized) path forward.
Ultimately, stop looking for "AI visibility" and start looking for "Attributable AI Citations." Whatever tool you pick, ensure they provide you with the raw data—not just the glossy, pre-baked charts. If you can’t answer the question, "What would I show in a weekly report?" using the tool's output, then keep looking.
Note: All pricing is excluded from this analysis. Contact vendor sales teams for enterprise-level quotes based on https://highstylife.com/how-do-i-track-domain-citations-across-ai-platforms/ your specific volume of queries and number of tracked languages.