Getting Started 📅 Updated 2026-06-12

Regarding Your First AI Discovery Report

Introduction

Your first AI Discovery Report is the most important document in your GEO strategy. It establishes your baseline: the factual starting point against which all future improvements will be measured. This guide explains every metric in the report, how to interpret them, and what actions to prioritize based on your results.

Key Concepts

AI Discovery Score (0–100): The composite score measuring your brand's overall AI visibility. Calculated from mention rate, citation rate, sentiment, prompt coverage, and cross-engine consistency.

Mention Rate: The percentage of prompts in your set where your brand appears in the AI-generated response. Higher is better.

Citation Rate: The percentage of mentions where the AI engine explicitly cites a specific source (URL, publication, domain) attributing information to your brand. High citation rate = high authority.

Sentiment Score: Whether AI mentions of your brand are positive, neutral, or negative in framing. Negative framing reduces consideration.

Prompt Coverage: The percentage of your target prompt set where your brand appears at least once across all engines.

Engine Breakdown: Your visibility performance segmented by individual AI engine (ChatGPT, Perplexity, Gemini, etc.).

Why It Matters

Without understanding your baseline report, you cannot prioritize GEO investments effectively. The report reveals: where you are already winning, where you are invisible, which engines perform best for your brand, and how you compare to competitors. These four dimensions define your entire optimization roadmap.

Step-by-Step Guidance

Step 1 — Review your overall AI Discovery Score Your score is a single number (0–100). Scores above 70 indicate strong AI visibility. Scores between 40–70 indicate moderate visibility with significant opportunity. Scores below 40 indicate near-invisibility in AI search.

Step 2 — Analyze your mention rate by engine Compare your mention rate across ChatGPT, Perplexity, Gemini, DeepSeek, and Claude. Identify: which engine mentions you most, which engine mentions you least, and the variance between them. Large variance often indicates engine-specific content gaps.

Step 3 — Examine your citation rate A high mention rate with low citation rate means AI engines are aware of your brand but do not have authoritative sources to cite. This indicates a content authority gap — the fix is creating citable content and earning third-party references.

Step 4 — Review prompt-level performance Drill down into individual prompts. Identify the 5 prompts where you rank highest and the 5 prompts where you rank lowest. The lowest-performing prompts represent your highest-opportunity targets.

Step 5 — Compare against competitors Review the competitor benchmarking section. For each competitor, note: their overall score vs. yours, which prompts they dominate that you don't, and which engines they outperform you on.

Step 6 — Review sentiment flags Check the sentiment analysis section. Any negative framing flags require immediate attention. Negative AI mentions can suppress consideration even among buyers who encounter your brand.

Step 7 — Build your priority action list Based on steps 1–6, create a ranked action list: (1) fix sentiment issues, (2) attack highest-opportunity prompt gaps, (3) build citation authority for the engines where your rate is lowest.

Best Practices

Do not compare your score to industry benchmarks yet. Your first report is your own baseline, not an industry comparison. Focus on relative gaps: your score vs. competitors, your top-performing engine vs. your worst.
Segment by prompt category. Review your score for comparison prompts separately from recommendation prompts — they often behave differently.
Screenshot or export your baseline. Before making any changes, preserve your baseline metrics. You need a clean before/after comparison.
Schedule your review cadence. After your first report, plan weekly check-ins for the first 8 weeks to track optimization impact.

Common Mistakes

Celebrating a high score without understanding the driver. A high score on branded queries but low score on category queries is a warning sign.
Ignoring low citation rate. Mentions without citations are fragile — they can disappear as AI models update.
Over-indexing on one engine. A high score on ChatGPT but near-zero on Perplexity leaves significant buyer segments uncovered.
Not acting on sentiment flags. Negative framing is the highest-urgency issue in any first report.

Practical Examples

A financial services firm receives their first report showing a 42 overall score. Breakdown: 68% mention rate on ChatGPT (strong) but 11% on Perplexity (very weak). Citation rate: 18% (low). They prioritize: (1) building authoritative content for Perplexity's preferred sources, (2) earning citations from financial media, and (3) addressing two sentiment flags from AI descriptions of their fee structure.

Summary

Your first AI Discovery Report is your strategic foundation. Understand each metric, identify your highest-leverage gaps, and build a prioritized action list before making any content or optimization changes. The report is most valuable when it drives clear, ordered action.