In the agency world, I spent years listening to stakeholders ask, “Are we showing up in AI?” and then watch them accept “visibility” as a metric. I’m done with that. If you can’t show me a conversion path from an LLM response to a CRM entry, it’s not a channel—it’s a vanity metric. When we talk about the 400m conversations found in modern conversation explorer tools, we aren't talking about "brand presence." We are talking about the raw, real user query data that defines the new top-of-funnel.
I’m writing this because too many platforms promise to “track everything” without disclosing their LLM sources, their data update cadence, or their specific engine coverage. Let’s look under the hood of these massive datasets and define what we actually do with them in a weekly report.
The 400M Conversation Benchmark: Why Data Depth Matters
When a vendor claims access to a database of 400m conversations, the first thing I ask is: Where is the telemetry coming from? Is it synthetic data generated by prompting models, or is it real user query data harvested from actual interactions across diverse AI search surfaces?
If you are looking at a conversation explorer tool, you need to understand that 400 million data points represent the "collective subconscious" of your target audience. This is the dataset used for:
- Intent Mapping: Identifying the specific phrasing users use when they skip Google and go straight to ChatGPT, Claude, or Perplexity. Knowledge Gap Analysis: Finding the questions the LLMs are failing to answer correctly about your brand. Prompt Engineering for SEO: Knowing exactly how to structure your documentation to be the "source of truth" in an AI-generated answer.
What would I show in a weekly report? I would show a "Query-to-Citation" trend line. If the 400m dataset shows a surge in specific problem-solving queries, and our site’s citation count in those responses isn’t increasing, our content strategy is failing.
Engine Coverage: A Necessary Reality Check
I keep a running list of engines that tools cover. A platform is only as good as its visibility into the specific LLMs that drive traffic to your industry. You cannot report on "AI search" as a monolith. You have to segment by engine.
Engine / Surface Coverage Status Data Source Notes ChatGPT (OpenAI) High Requires RAG-specific monitoring Perplexity Medium Source-dependent via citation tracking Gemini (Google) Medium Often overlaps with Search Generative Experience Claude (Anthropic) Low Primarily gated/API access challengesIf a tool claims to track "all AI search," check the methodology. Are they scraping the web? Are they using API hooks? If they don't provide a list of sources, you are playing blind.
Brand Mentions vs. Citations vs. Share of Voice
Here is where most strategies go off the rails. Let’s define these so we can actually build a dashboard:
Brand Mentions: The LLM talks about you. This is irrelevant for revenue unless it drives a search. Citations: The LLM links to your site. This is your primary "AI-SEO" metric. It is the closest thing to an organic backlink in the LLM ecosystem. Share of Voice (SoV): The percentage of times your brand is mentioned or cited compared to competitors within a specific cluster of the 400m conversations.In your reports, do not conflate these. A high mention rate with zero citations is a brand awareness campaign, not a performance channel. You need to treat AI visibility as a measurable revenue channel, which brings us to the integration layer.
Integrating with GA4 and Adobe Analytics
I am tired of seeing "AI Attribution" that isn't connected to the tech stack. If you aren't using GA4 integration or Adobe Analytics integration to track traffic derived from AI search, you are just guessing.
When you pull data from a conversation explorer, you must pass that data through a tagging schema. Here is the workflow I implement:
- Step 1: Use UTM parameters specific to the AI source (e.g., utm_source=perplexity_ai). Step 2: Map these sources in GA4/Adobe Analytics as distinct channels. Step 3: Compare the conversion rate of "AI-referred" traffic against traditional organic search traffic.
If the AI-referred traffic has a higher bounce rate, your landing page isn't matching the conversational intent query fanouts of the LLM’s answer. You need to align your content depth with the specific conversational queries found in the 400m dataset.
The Competitive Landscape: Who is Building What?
The market is crowded, and much of the positioning is pure fluff. I track three key players, each approaching the 400m conversation depth differently:
Semrush
As the incumbent, Semrush approaches the space with a legacy of massive web-crawling infrastructure. Their strength lies in the depth of their existing keyword databases and their ability to bridge the gap between traditional SEO and AI-driven search results. They are the safe choice for enterprise teams that need a unified view of both search paradigms.
Peec AI
Peec AI focuses heavily on the conversational aspect, looking at how user queries are evolving in real-time. Their value proposition is centered on the agility of their data—understanding the "shift" in user intent before it hits the broader search market. They are moving away from static keyword reports toward dynamic conversational insights.
Otterly AI
Otterly AI is a niche player that digs deep into the actual citation loop. They focus on the technical implementation of how brands appear in AI-generated answers. Their strength is in the granular monitoring of how often a link is actually included in the final output of an LLM, making them a tactical choice for technical SEOs.
Note: A common mistake in the industry is the assumption of pricing-per-feature. In the research for this post, no pricing numbers were included in the scraped or public-facing documentation for these tools. I will not invent numbers for you. If you want to know the cost, talk to sales, not a blog post.
The Weekly Report: What Actually Goes In?
When I run reporting for multi-market brands, the "Conversation Explorer" insights go into a specific slide deck. Here is what I include every Monday morning:
Metric Definition Business Impact AI Share of Voice Brand presence in top 400m conversation clusters Competitive benchmarking Citation Velocity Growth of direct links from LLMs Referral traffic potential AI-Driven Conversions Attributed revenue via GA4/Adobe Budget justificationIf you aren't reporting on these three things, you aren't managing a channel. You are just reading a dashboard. The 400m conversations exist to give you a map of the market—use them to find where your users are asking questions, and ensure your site is the one providing the answer that gets cited.
Stop chasing the buzzwords. Start tracking the engines, the citations, and the conversions. That is how you win in the era of AI search.