Discover

The Discover section gives you deeper analytical tools to understand how AI engines see your brand beyond the topline metrics.

Citations

Citations track which domains AI engines reference when discussing brands in your market. This reveals the web sources that feed AI recommendations.

What You'll See

• Top cited domains — which websites AI engines link to most when answering queries in your category
• Your citation share — how often AI engines cite your domain vs competitors
• Citation types — editorial sites, social platforms, review sites, and direct brand mentions
• Trends — how citation volumes change over time

Why It Matters

AI engines don't just generate answers from training data — they actively reference and cite web sources. If your domain isn't among the sources AI engines cite, you're missing a key visibility channel. The citations view helps you identify where to seek coverage, backlinks, and mentions.

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Keywords

Keywords reveals the sub-queries AI engines generate internally when answering your monitored prompts. This is called fan-out — when an AI engine receives a complex question, it decomposes it into smaller searches before synthesizing a response.

What You'll See

• Sub-query volume — how many internal searches your prompts generate
• Purpose breakdown — which sub-queries mention your brand, competitors, or general topics
• Competitor comparison — keyword volume benchmarked against competitors
• Browse view — the full list of generated sub-queries

Why It Matters

Fan-out queries reveal what information AI engines are actually looking for when they answer questions about your market. If you create content that directly answers these sub-queries, you're more likely to be cited and recommended. Think of it as keyword research, but for AI engines instead of traditional search.

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Map

The Semantic Map plots your brand and competitors on custom axes to visualize how AI engines position you relative to the market.

What You'll See

• An interactive scatter plot with your brand and competitors as data points
• Two configurable axes representing semantic dimensions (e.g., "Budget vs Premium," "Simple vs Feature-Rich," "SMB vs Enterprise")
• System presets for common positioning dimensions
• Filters by persona, location, topic, and product

How to Use It

1. Choose your axes using the presets or create custom dimensions that matter to your positioning
2. Read your position — where AI engines place you tells you how they understand your brand
3. Compare with intent — if you position yourself as "Enterprise" but AI engines place you closer to "SMB," there's a perception gap to address
4. Filter by persona — different audiences may perceive your brand differently

Why It Matters

The Map makes abstract AI perceptions concrete. Instead of wondering "how do AI engines see us?", you can see exactly where you land on the dimensions that matter to your business.

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AI Perception

AI Perception runs a deep scan across all your monitored prompts to understand what AI engines collectively believe about your brand.

What You'll See

• Perception themes — the key attributes and characteristics AI engines associate with your brand
• Platform comparisons — how perceptions differ across ChatGPT, Perplexity, Gemini, and others
• Validated prompts — queries where your content strategy is clearly working
• Library gaps — personas, topics, or products that AI engines associate with your market but that you're not currently monitoring

Why It Matters

Your topline metrics tell you how much visibility you have. AI Perception tells you what kind of visibility — what AI engines actually understand about your brand, where that understanding is accurate, and where it's incomplete or wrong. Library gap suggestions also help you expand your monitoring to cover blind spots.