The First Platform to Reveal Real AI Search Demand

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 120M+ 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. 

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 120M+ 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.

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.

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.

How We Predict AI Search Volume Accurately: Our Ensemble Approach

Beyond solving the core intent matching problems outlined above, our accuracy comes from a dynamic ensemble methodology that balances multiple data signals while correcting for inherent biases:

Step 1: Multi-Source Signal Collection

Step 2: Dynamic Source Weighting Based on Query Context

Each source gets weighted differently based on how reliable it’s been for similar queries and how relevant it is to the specific intent.

Step 3: Bias Detection and Correction

Where:

• D(x): Domain bias function (negative for biased queries, zero for balanced)
• T(x,t): Temporal stability function (negative for anomalous spikes, zero for stable)
• A(x): Source agreement metric (positive for strong consensus, zero for average agreement)
• ε_noise: Small stochastic adjustment for uncertainty
• exp(): When components are negative, CF < 1 (reduces estimate); when positive, CF > 1 (increases estimate)

Step 4: Final Ensemble Prediction

The 120M+ real AI conversations form our core signal (S₁), while public and partner data provide validation and broader market context. The correction factor ensures no single source distorts the final prediction.

Real-world bias corrections in action:

• Overestimation Prevention: Our conversational data shows high volume but public signals suggest niche interest → domain_correction reduces the estimate to prevent overestimating niche topics
• Anomaly Detection: One data source shows a sudden volume spike while others remain stable → stability_filter dampens the anomalous spike
• Domain Bias Correction: Some datasets skew heavily toward certain domains (like tech, finance, or marketing) → domain_correction applies reduction factors to ensure predictions reflect true market demand across all industries

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]

Niyati Mahale

Niyati Mahale is a Content Writer @Writesonic. She specializes in artificial intelligence and B2B, with a flair for combining effective storytelling and SEO best practices to create impactful content.