• AI engines (ChatGPT, Perplexity, Gemini, Claude, Copilot) are generating brand recommendations in response to commercial, research, and decision-stage queries
• These recommendations are shaped by entity strength, citation patterns, narrative consistency, and prompt coverage - not by keyword density or domain authority
• Most businesses have no visibility into how they or their competitors appear in this layer

• A buyer asking "what's the best [category] tool for [use case]" receives a curated AI answer - before visiting any website
• The brands named in that answer capture attention, trust, and intent at the highest-leverage moment in the decision process
• Brands absent from that answer are invisible to a decision already in progress

• Competitive advantage in AI is not about who has the best content - it's about who has the most coherent, trusted, and widely-cited presence across the sources AI systems draw from
• The competitive gap is largely invisible to businesses still measuring only Google rankings and web traffic

AI Adoption as a Decision Layer

What AI Systems Use to Select Brands

Competitive Visibility Gap: Simulation

The CANE Intelligence Framework for AI Competitor Analysis

• Build a prompt set of 40–80 queries across: awareness-stage, comparison-stage, and decision-stage
• Run each prompt across at least 3 AI engines (ChatGPT, Perplexity, Gemini minimum)
• Record which competitors appear, in what position, and with what framing
• Calculate each competitor's prompt coverage rate: the percentage of relevant prompts where they are named

• Identify which publications, review platforms, forums, and databases AI engines draw from when answering category queries
• Map which competitors are cited in those sources - and how prominently
• Identify sources where you are absent but competitors are present
• Identify sources where you are present but not being cited by AI (a signal of weak entity linkage)

• For each competitor named in your prompt set, record the descriptive language AI uses
• Identify: what problem do they solve (per AI)? What differentiator do they own? What use case are they associated with?
• Compare this to their actual positioning - gaps between intended and AI-perceived narrative are competitive intelligence
• Identify narrative gaps in your own category: positions that no competitor currently owns in AI answers

• Test each competitor's entity by asking direct questions: "What does [Competitor] do?" "Who is [Competitor] best for?" "What are [Competitor]'s strengths and weaknesses?"
• Score consistency across engines (1–5 scale per engine, average across 3 engines)
• Identify which competitors have strong entity presence and which are fragmented or absent
• Assess your own entity strength against the competitive field

1. A prompt coverage leaderboard (who appears most, in what contexts)
2. A citation source gap analysis (where you need to build presence)
3. A narrative ownership map (what positions are taken, what's available)
4. An entity strength ranking (who AI trusts most, and why)

(Simulation) AI Competitor Analysis: B2B Project Management Software Category

• Competitor A is cited in 6 major industry publications, 3 review platforms, and 2 analyst databases that AI engines draw from consistently
• Company X is present on 2 review platforms but absent from industry publications and analyst databases
• The citation gap directly explains the coverage gap

1. Build presence in 4 specific industry publications and 1 analyst database (authority gap)
2. Claim the "mid-market teams scaling from startup to enterprise" narrative position (narrative gap - no competitor owns it)
3. Strengthen entity signals through structured data, consistent cross-source descriptions, and Wikipedia/Wikidata presence (entity gap)

1. Define your prompt universe. Write 40–60 prompts that represent real buyer questions in your category. Include awareness queries ("what is the best [category] for [use case]"), comparison queries ("compare [you] vs [competitor]"), and decision queries ("which [category] tool should I use if I need [specific outcome]"). Do not guess - use actual customer language from sales calls, support tickets, and review sites.
2. Select your AI engine set. At minimum: ChatGPT (GPT-4o), Perplexity, and Google Gemini. Add Claude and Microsoft Copilot if your category has enterprise buyers. Run each prompt fresh (no conversation history) in each engine.
3. Record outputs systematically. For each prompt × engine combination, record: which brands are named, in what order, with what descriptive language, and whether a citation is provided. Use a spreadsheet. This is your raw data.
4. Calculate prompt coverage rates. For each competitor (and yourself), calculate: (prompts where named ÷ total prompts) × 100. This is your coverage rate. Rank all competitors. This is your competitive baseline.
5. Map the citation sources. For prompts where citations are provided, record the source. Build a list of the top 10–15 sources AI engines draw from in your category. Cross-reference: which competitors are present in each source? Where are you absent?
6. Extract and compare narratives. Pull the descriptive language AI uses for each competitor. Identify: what problem do they solve, what use case do they own, what differentiator is attributed to them? Map this against your own AI-assigned narrative. Identify open positions.
7. Run entity strength tests. For each major competitor, ask direct entity questions across all three engines. Score consistency 1–5. Average across engines. Compare to your own score.
8. Build your AI competitive map. Synthesize the four CANE dimensions into a single strategic document: coverage leaderboard, citation gap list, narrative ownership map, entity strength ranking.
9. Prioritize three actions. From the map, identify the highest-leverage gaps: the citation source you can enter fastest, the narrative position you can claim most credibly, and the entity signal you can strengthen most immediately. Prioritize these over generic content production.
10. Set a re-measurement cadence. AI competitive positioning shifts as new content is published, new citations are indexed, and AI models are updated. Re-run your prompt set every 60–90 days. Track coverage rate changes over time. This is your ongoing competitive intelligence system.

• LinkedIn post: "We tracked 60 AI prompts in our category. Our biggest competitor appeared in 74% of answers. We appeared in 19%. Here's what we found - and what we're doing about it."
• Short insight: "AI competitor analysis is not keyword research. It's prompt coverage mapping, citation source auditing, and narrative ownership analysis."
• Report section: "AI Competitive Landscape: Prompt Coverage, Citation Authority, and Narrative Positioning Across Key Competitors"
• Presentation slide: "The CANE Framework: How We Map Competitive Positioning in AI Answers"

Find Out Exactly Where Your Competitors Stand in AI - And Where You Don't