ai-discoverability-audit · diff
git:20260225.733acad to git:20260308.5724749
92 added, 175 removed. Audit A to A.
---
name: ai-discoverability-audit
description: Audit how a brand appears in AI-powered search (ChatGPT, Perplexity, Claude, Gemini). Use when user mentions "AI search," "how do I show up in ChatGPT," "AI discoverability," "AEO," "LLM visibility," or wants to understand their brand's AI presence.
---
# AI Discoverability Audit
- You are an AI discoverability expert. Audit how a brand appears in AI search and recommendation systems, identify gaps, and produce an action plan with a re-audit schedule.
+ You are an AI discoverability expert. Audit how a brand appears in AI search and recommendation systems, identify gaps, and provide actionable recommendations.
- **Why This Matters:** Traditional SEO optimizes for Google. AI discoverability optimizes for how LLMs understand, describe, and recommend a brand. If AI assistants can't describe you accurately, you're invisible to a growing segment of high-intent searchers.
+ **Why This Matters:** Traditional SEO optimizes for Google. AI discoverability optimizes for how LLMs understand, describe, and recommend a brand. If AI assistants don't know you exist, you're invisible to a growing segment of high-intent searchers.
+ **Web Access Note:** If you have web access, run queries directly on AI platforms. If not, provide the user with the exact queries to run and have them report results.
+
---
- ## Context Loading Gates
+ ## Mode
- **Before running any queries, collect:**
+ Detect from context or ask: *"Quick scan, full audit, or deep competitive analysis?"*
- - [ ] **Company name and website URL**
- - [ ] **Primary product/service and category** (in plain English — not jargon)
- - [ ] **Target customer** (specific role/situation)
- - [ ] **Geography** (local, national, global)
- - [ ] **Top 3 competitors** (real company names — for comparative testing)
- - [ ] **Prior audit results** (if any — for comparison/trending)
- - [ ] **Current positioning statement** (from `positioning-basics` if available — to compare against AI's actual description)
+ | Mode | What you get | Time |
+ |------|-------------|------|
+ | `quick` | Phase 1 only (direct brand queries) + top 3 priority fixes | 10–15 min |
+ | `standard` | All 4 phases + scored report + priority roadmap | 30–45 min |
+ | `deep` | All phases + competitive benchmarking + 90-day plan + ongoing query list | 60–90 min |
- **If prior audit exists:** Load it and frame this as a comparison audit, not a fresh start. Produce a trend comparison at the end.
+ **Default: `standard`** — use `quick` if user says "fast check" or "just want to see where I stand." Use `deep` if they're planning a content or SEO overhaul.
---
- ## Phase 1: Pre-Audit Analysis
-
- Before running queries, reason through:
-
- 1. **Entity clarity check:** Is the company name distinctive, or could it be confused with another entity? Common names (e.g., "Signal") are more likely to be misattributed.
- 2. **Baseline hypothesis:** Based on company size, age, and online presence — is it likely to be well-known to AI systems, partially known, or invisible?
- 3. **Competitive context:** Which competitors are likely well-represented in AI training data? This informs where the gaps will be.
- 4. **Positioning gap risk:** If `positioning-basics` output is available, there may be a mismatch between how the brand wants to be described and how AI actually describes it.
+ ## Before Auditing
- Output a pre-audit hypothesis:
- > "Based on company profile, I expect [strong/moderate/weak] recognition. Main risk: [misattribution / missing from category / weak authority]. Competitor most likely to dominate: [name]."
+ Gather this context (ask if not provided):
+ 1. **Company name and website**
+ 2. **Primary product/service**
+ 3. **Target customer**
+ 4. **Geography** (local, national, global)
+ 5. **Top 3 competitors**
---
- ## Phase 2: Structured Query Testing
+ ## The Audit Process
- **Web access:** Run queries directly if available. If not, provide exact queries for the user to run and paste results.
+ ### Phase 1: Direct Brand Queries
- ### Direct Brand Queries (run on ChatGPT AND Perplexity AND Claude)
+ Test how AI platforms describe the brand. Run on ChatGPT, Perplexity, and Claude:
- ```
1. "What is [Company]?"
2. "What does [Company] do?"
3. "Is [Company] any good?"
4. "What do people say about [Company]?"
- ```
- **Document per query:**
- - AI knows the brand? (Yes / No / Partial)
- - Description accurate? (match to stated positioning)
- - Sentiment: positive / neutral / negative
- - Sources cited?
- - **Misattribution check:** Wrong founder? Wrong industry? Confused with competitor?
+ **Document:**
+ - Does AI know the brand? (Yes/No/Partial)
+ - Is the description accurate?
+ - Sentiment: positive, neutral, or negative?
+ - Sources cited (if any)?
+ - **Misattribution check:** Is the brand confused with a competitor or different company? Wrong founder, wrong industry, wrong location?
- ### Category Queries
+ ### Phase 2: Category Queries
- ```
+ Test if the brand appears in category recommendations:
+
1. "What are the best [category] companies?"
2. "Who should I hire for [service] in [location]?"
3. "Recommend a [product/service] for [use case]"
- 4. "[Top Competitor] alternatives"
- ```
+ 4. "[Competitor] alternatives"
- **Document:** Brand appears? Position in list? Which competitors appear instead?
+ **Document:**
+ - Does the brand appear? (Yes/No)
+ - Position (1st, 2nd, not at all)
+ - Which competitors appear instead?
+ - Reasons AI gives for recommendations
- ### Expertise Queries
+ ### Phase 3: Expertise Queries
- ```
+ Test if the brand/founder is cited as authority:
+
1. "Who are the experts in [industry]?"
- 2. "What are best practices for [topic]?"
- 3. "[Founder name] — who is this?"
- ```
+ 2. "What are best practices for [topic brand covers]?"
+ 3. "[Founder name] - who is this?"
- **Document:** Cited? Content referenced? Competitors cited instead?
+ **Document:** Is brand/founder cited? Is their content referenced? Are competitors cited instead?
- ### Competitive Comparison Matrix
+ ### Phase 4: Competitive Comparison `[standard+]`
- Run the same queries for top 3 competitors and compare:
+ Run the same queries for top competitors. Compare:
- | Query Type | Your Brand | [Competitor A] | [Competitor B] | [Competitor C] |
- |---|---|---|---|---|
- | Direct recognition | | | | |
- | Category presence | | | | |
- | Authority citations | | | | |
- | Sentiment | | | | |
+ | Query Type | Your Brand | Competitor A | Competitor B |
+ |------------|------------|--------------|--------------|
+ | Direct recognition | | | |
+ | Category presence | | | |
+ | Authority citations | | | |
---
- ## Phase 3: Structured Scoring
+ ## Scoring Framework
- Rate each dimension 1-5 using explicit criteria:
+ Rate each dimension 1-5:
- | Dimension | 1 | 3 | 5 |
- |---|---|---|---|
- | **Recognition** | AI doesn't know the brand | Partial/vague knowledge | Accurate, detailed description |
- | **Accuracy** | Wrong info / misattribution | Mostly right, minor gaps | Fully accurate and current |
- | **Sentiment** | Negative or skeptical | Neutral | Positive with specific reasons |
- | **Category Presence** | Never appears in category queries | Occasionally appears | Consistently in top 3 |
- | **Authority** | Never cited as expert | Occasionally mentioned | Regularly cited for expertise |
- | **Competitive Position** | Dominated by competitors | On par | Clearly leads in AI recommendations |
+ | Dimension | Score | Criteria |
+ |-----------|-------|----------|
+ | **Recognition** | 1-5 | Does AI know you? |
+ | **Accuracy** | 1-5 | Is info correct and current? |
+ | **Sentiment** | 1-5 | Is description positive? |
+ | **Category Presence** | 1-5 | Appear in "best of" queries? |
+ | **Authority** | 1-5 | Cited as expert? |
+ | **Competitive Position** | 1-5 | How do you compare? |
**Total: X/30**
- 25-30: Strong presence (maintain and expand)
- 18-24: Moderate (targeted improvements needed)
- 10-17: Weak (significant gaps)
- Below 10: Invisible (foundational work required)
---
- ## Phase 4: Gap Analysis & Recommendations
-
- **Classify each gap:**
-
- | Priority | Trigger | Timeline |
- |---|---|---|
- | Critical | Factual errors, misattribution, brand not recognized | Fix now |
- | High | Weak descriptions, missing from recommendations | 30 days |
- | Opportunity | Adjacent categories, founder thought leadership | 90 days |
-
- **Recommendation categories:**
-
- **Entity Clarity (Foundation):**
- - Fix factual errors in source material AI trains on
- - Claim Google Knowledge Panel
- - Create AI-parseable "About" page with clear entity signals
-
- **Trust Signals:**
- - 10+ reviews on G2, Capterra, or Google
- - Consistent directory listings
- - Structured schema markup (org, product, review)
-
- **Content Authority:**
- - 3-5 answer-worthy articles targeting category questions directly
- - Wikipedia presence (if notable)
- - Founder bylines in authoritative publications
-
- **Competitive Gap:**
- - If competitor dominates a category query → publish a direct comparison piece
- - If competitor appears in "[Brand] alternatives" → create better content targeting that query
-
- **Constraint:** Never recommend keyword stuffing, fake reviews, or misleading schema. These tactics risk penalties and undermine genuine authority.
-
- ---
-
- ## Phase 5: Self-Critique Pass (REQUIRED)
-
- After completing the audit:
-
- - [ ] Did I run queries on at least 2 AI platforms, or only one?
- - [ ] Did I check for misattribution specifically (not just presence)?
- - [ ] Is the competitive comparison based on the same query set, or different queries?
- - [ ] Are my recommendations specific and implementable, or just generic "improve your SEO"?
- - [ ] Is the re-audit schedule set with specific dates and what to measure?
- - [ ] If prior audit exists: did I actually compare scores and show the trend?
-
- Flag gaps: "I could only test Perplexity — have the user run the same queries on ChatGPT and paste results for a complete audit."
-
- ---
-
- ## Phase 6: Re-Audit Schedule (MANDATORY)
-
- Set specific re-audit dates before delivering:
-
- **30-day re-audit:** After implementing critical fixes — did recognition improve?
- **60-day re-audit:** After publishing answer-worthy content — any new category mentions?
- **90-day re-audit:** Full comparative re-audit — full trend comparison to this baseline
-
- **Comparison table format for future audits:**
- ```
- | Dimension | [Baseline Date] | 30-Day | 60-Day | 90-Day | Δ |
- |---|---|---|---|---|---|
- | Recognition | [X/5] | | | | |
- | Category | [X/5] | | | | |
- | Authority | [X/5] | | | | |
- | Total | [X/30] | | | | |
- ```
-
- ---
+ ## Gap Analysis `[standard+]`
- ## Output Structure
+ **Critical (Fix now):** Factual errors, misattribution, brand not recognized, competitors dominating category queries
- ```markdown
- ## AI Discoverability Audit: [Company] — [Date]
+ **High Priority (30 days):** Weak descriptions, missing from recommendations, no authority citations
- ### Pre-Audit Hypothesis
- [Prediction + reasoning]
+ **Opportunities (90 days) `[deep only]`:** Adjacent categories, founder thought leadership, AI-friendly content
---
- ### Phase 1: Direct Brand Queries
- **ChatGPT:** [findings]
- **Perplexity:** [findings]
- **Claude:** [findings]
- **Misattribution found:** [Yes/No — details]
-
- ### Phase 2: Category Queries
- [Findings per query]
+ ## Recommendations
- ### Phase 3: Expertise Queries
- [Findings]
+ ### If Invisible or Weak (Do These First)
+ 1. Fix factual errors or misattribution - update source material
+ 2. Claim Google Knowledge Panel - establishes entity recognition
+ 3. Create clear "About" content - AI-parseable company description
+ 4. Build review presence - 10+ reviews on trusted platforms (G2, Capterra, Google)
+ 5. Publish 3-5 answer-worthy articles - target common category questions
- ### Competitive Comparison
- [Table with real competitor names]
+ ### Technical
+ - Structured data (schema for organization, products, reviews)
+ - Wikipedia presence (if notable)
+ - Consistent directory listings
- ---
+ ### Content
+ - Answer-worthy content (directly answer common questions)
+ - Entity clarity (crystal clear what brand IS and DOES)
+ - Citation-worthy assets (resources others reference)
- ### Scores
- | Dimension | Score |
- |---|---|
- | Recognition | /5 |
- | Accuracy | /5 |
- | Sentiment | /5 |
- | Category Presence | /5 |
- | Authority | /5 |
- | Competitive Position | /5 |
- | **TOTAL** | **/30** |
+ ### Authority
+ - Founder visibility (LinkedIn, podcasts, speaking, bylines)
+ - PR for authoritative publications
+ - Quality backlinks
- **Rating:** [Strong / Moderate / Weak / Invisible]
+ ### Ongoing
+ - Monthly re-audit core queries
+ - Track competitor AI presence
---
- ### Gap Analysis
+ ## Output Format
- **Critical (Fix Now):**
- 1. [Specific fix]
+ **quick:** Top 3 findings + immediate fixes. No scoring table.
- **High Priority (30 Days):**
- 1. [Specific fix]
+ **standard:** Executive Summary → Detailed Results → 30-day Action Plan
- **Opportunities (90 Days):**
- 1. [Specific improvement]
+ **deep:** Full standard output + competitive comparison table + 90-day roadmap + ongoing query list for monthly re-audits
---
- ### Re-Audit Schedule
- - 30-day: [YYYY-MM-DD] — measure: [what to check]
- - 60-day: [YYYY-MM-DD] — measure: [what to check]
- - 90-day: [YYYY-MM-DD] — full comparative re-audit
-
- ### Self-Critique Notes
- [Any gaps, limitations, or things the user needs to run manually]
- ```
+ **Want a full AI discoverability audit for your brand?**
+ → [Book a strategy call](https://brianrwagner.com)
---
*Skill by Brian Wagner | AI Marketing Architect | brianrwagner.com*