You know your customers are asking AI about your industry, but without knowing the actual volume behind each prompt, you can’t prioritize what matters most.
Remember when keyword volume changed everything for SEO?
You could finally see which search terms actually mattered. No more guessing. No more shooting in the dark. Just real data showing exactly how many people searched for “project management software” vs. “team collaboration tools.”
Today, we’re doing the same thing for AI search.
We’re excited to announce two game-changing features for GEO (Generative Engine Optimization): AI Search Volume and Prompt Explorer.
For the first time, you can see exactly how many people are asking specific questions across ChatGPT, Claude, Perplexity, and other AI platforms.
The Problem: You Don’t Know What to Prioritize in AI Search Optimization
Here’s what most brands are doing wrong with AI optimization:
- Guessing at prompts instead of tracking real user language
- Missing commercial intent hidden in long-tail AI queries
- Optimizing for keywords when users ask full questions to AI
- No way to prioritize which AI prompts actually drive volume
Sound familiar? It’s exactly where SEO was 20 years ago, before we had keyword volume data.
The Solution: Prompt Explorer with AI Search Volume Data
Our new Prompt Explorer combines AI prompt discovery with real search volume data, giving you both the questions to target and the volume to prioritize them.
It’s your AI prompt research tool that reveals exactly what questions people ask AI platforms about your industry. For every prompt you enter, you can find the total volume and its country-wise distribution.
That’s what makes it revolutionary: AI Search Volume.
Our new AI Search Volume feature shows you exactly how many people ask each prompt across all major AI platforms on a month-on-month basis. Powered by our dataset of 200M+ real AI conversations, we can predict monthly volume for any AI prompt, even highly specific ones like:
- “best CRM for remote startup with 15 people” → 5770 monthly queries
- “project management software for agencies under $50/month” → 6600 monthly queries
- “what’s the most user-friendly tool to manage client projects” → 33810 monthly queries
This isn’t guesswork. Our predictions are based on patterns observed from real user behavior across platforms like ChatGPT, Claude, Perplexity, and other platforms to give you the most accurate AI search volume predictions available.
How are we able to predict AI search volume accurately?
What makes our AI search volume predictions so reliable? We solve two critical problems that traditional keyword volume data can’t address for AI search optimization.
Problem 1: Keyword Volume Doesn’t Translate to AI Prompt Volume
People use Google and AI platforms with largely different intent.
For example, when searching navigational queries like “Monday.com login” or “Asana pricing,” users go to Google because they want direct links. These queries get millions of Google searches but virtually zero AI usage.
Meanwhile, commercial and generational intent queries like “Create a LinkedIn Post on AI in Marketing” have lower Google search volume but significant AI platform usage.
The volume translation isn’t 1:1; it depends entirely on the intent behind each query.
Many tools fail to consider this and rely on keyword data for prompt data. They assume if “project management tool” has high Google volume, it must have high AI volume too.
But such an assumption regarding all keyword volume translating into AI Chat Volume is far from accurate. Our ensemble approach combines keyword-based estimates with our 200M+ conversational dataset to correct for these behavioral differences between search and chat.
Problem 2: Long-Tail Fragmentation Across Variations
As soon as you move beyond simple prompts, search volume begins to fragment fast. In traditional keyword research, this was a known issue.
A single concept like “project management software” might show up under 10-15 different keyword variations:
- “project planning tools”
- “PM software”
- “team task tracking tool”
Sure, some volume got split across them, but you could reasonably guess each one of them and map them out.
With AI prompts, that challenge multiplies because people don’t use short keywords. They speak in full, natural sentences, often 20+ words long, with endless ways to express the same idea.
Let’s say a user wants software to manage a remote team. They might frame that single intent in prompts like:
- “which is the best project management tool for people working remotely”
- “project management tool that’s good for work from home teams”
- “what’s the top PM software for distributed teams”
- “recommend project management software for remote employees”
And hundreds more. Each one looks different, but the underlying need is exactly the same.
The Real Problem: You Can’t Measure What You Can’t Find
If you’re trying to measure AI search demand using traditional methods, this becomes nearly impossible:
- You don’t know all the prompt variations that exist
- You have to count each variation separately
- You miss most of them entirely
- And you end up drastically underestimating the actual opportunity
With AI prompting, the fragmentation problem scales far beyond what keyword tools were ever built to handle.
Our Solution: Unified Intent Demand
We don’t expect you to count every prompt variation. In fact, we know that’s not realistic.
Instead, our system handles it for you by clustering prompts based on semantic intent, not just keywords.
Here’s how it works. You enter any variation of a prompt:
- Identifies the user intent behind your prompt
- Finds all semantic variations people actually use to express that same intent
- Aggregates volume across all variations to show unified market demand
- Gives you one number representing total addressable volume for that intent
So if you search “best project management tool for remote teams,” we automatically include:
- “what’s the top PM software for distributed teams”
- “tools to manage remote work projects”
- “recommend a platform for remote team collaboration”
- and hundreds of other variations
You don’t need to guess them; we already count and aggregate the volume for each. And instead of a scattered list of long-tail prompts with low volume, you see one total demand signal for the full intent.
You focus on what your audience wants. We surface all the ways they’re actually asking for it.
Our Ensemble Approach
Estimating prompt volume across AI platforms isn’t just a matter of counting. Unlike keyword-based search, prompts vary in length, structure, and phrasing, even when the underlying intent is identical.
To produce accurate and reliable volume estimates, we use a multi-source ensemble system that balances behavioral signals, public market indicators, and enterprise datasets.
This helps us capture the full spectrum of user demand while correcting for blind spots in any one source.
Here’s how it works:
1. We source signals from multiple independent channels:
- Conversational data from 200M+ real AI interactions
- Public forums and knowledge platforms (e.g. Reddit, Stack Overflow, etc)
- Enterprise-grade partner data
2. We weigh those sources contextually
Some sources are stronger for certain domains (e.g. public forums in tech, partner data in eCommerce).
Our system dynamically adjusts the influence of each input based on its historical reliability and relevance for the prompt being analyzed.
3. We correct for bias and noise:
Sometimes a trend appears inflated due to hype, spikes, or platform-specific skews. Our system corrects for such biases to give you an accurate number instead of simply considering it at face value.
4. The result: A reliable, unified volume estimate
All of this comes together in a final prediction formula:
Volume Estimate = f(Proprietary data, Public data, Partner data) ± Correction factor
The end result is a prompt volume number that reflects true user demand, not just keyword echoes or raw prompt counts.
The Result
While most competitors rely on a single source or average volumes blindly, our step-by-step ensemble methodology with mathematical bias correction ensures your decisions are grounded in statistically validated opportunity, not distorted projections.
See How You Can Use Prompt Explorer
Let’s walk through how you can use Prompt Explorer to uncover real AI search demand and turn it into a data-backed content or GEO strategy.
Say you enter: “What are the best design tools” and click Research.
Prompt Explorer immediately shows you the estimated monthly AI search volume across platforms.
For “What are the best design tools”:
- 31.2k average monthly volume across AI platforms
- Medium usage frequency suggests this is a trending topic
- Top competing brands: Canva, Adobe Firefly, Figma, Sketch, Adobe Express
- Global reach: High volume across India, US, UK, Canada, and Australia
The trend shows you how the global interest in this prompt has changed over the past 12 months, helping you understand whether it’s gaining momentum, staying consistent, or declining.
Below that, the region-wise distribution shows you where the average monthly demand is highest, so you know which markets are most engaged.
Getting Started
AI Search Volume and Prompt Explorer are now live for all Enterprise plan users.
Ready to stop guessing and start knowing?
- See exactly which AI prompts drive real volume
- Discover opportunities your competitors are missing
- Make data-driven GEO decisions for the first time
- Track your AI visibility improvements over time
The early movers in SEO dominated search for 15+ years. This is that moment for AI search.
Questions about AI Search Volume or Prompt Explorer? Our team is here to help. Reach out to [email protected]
