How to Optimize for AI Search: A 9-Point Evaluation Checklist

Knowing you need to optimise for AI search and knowing whether you're actually doing it well are two different things. This checklist gives you a structured way to evaluate where you stand across nine criteria that determine AI search visibility.

Work through each item honestly. The goal isn't to feel good - it's to identify the specific gaps worth fixing.

1. AI Visibility Baseline Across Multiple Models

What to evaluate: Have you tested your brand's visibility across ChatGPT, Perplexity, Claude, and Gemini? Do you know your mention rate across your target queries?

What good looks like: You have a documented baseline. You know your mention rate (what % of target queries mention you), which queries you win, which you lose, and which competitors appear when you don't.

What bad looks like: You've tested once manually in ChatGPT and haven't done it again. You have no tracking system. You don't know whether your visibility is improving or declining.

Red flag: You're visible in ChatGPT but invisible in Perplexity (or vice versa), and you don't know why. Different systems have different mechanisms - a gap in one is not the same as a gap in all.

Fix: Set up systematic tracking. Bingly tests your visibility across multiple AI models on a schedule and tracks trends over time.

2. Entity Clarity: What Does AI Think You Do?

What to evaluate: Ask ChatGPT or Claude "what does [your brand] do?" and "who is [your brand] best for?" Are the answers accurate and specific?

What good looks like: The model gives a clear, accurate description of your product, names the correct product category, and identifies the right user personas. No confusion, no outdated information.

What bad looks like: The model's description is vague ("a software company that helps businesses improve efficiency"), wrong category, or describes a product version from two years ago.

Red flag: The model confuses you with a competitor or a different company with a similar name. This is an entity clarity crisis - fix it before anything else.

Fix: Rewrite your homepage, About page, and key product pages to include explicit entity language: your exact product category, who you serve, what specific problems you solve. Add Organisation schema markup.

3. Use-Case Content Coverage

What to evaluate: For each of your major use cases, do you have a dedicated, detailed content piece? Not a feature page - a genuine guide addressing that specific use case with expert-level depth.

What good looks like: You have 5-10 use-case-specific guides. Each one directly addresses a specific buyer scenario. The content is detailed enough that an AI model would use it as a primary source when someone asks about that use case.

What bad looks like: You have one generic "features" page and a few surface-level blog posts. Your content could describe 30 different products in your category.

Red flag: Competitors have detailed use-case content and you don't. Check by searching Perplexity for your top use-case queries and seeing whose content gets cited.

Fix: List your top 5 use cases. Publish one expert-level guide per use case (1,500+ words, specific, practical). This is your highest-leverage content investment for AI visibility.

4. Third-Party Review Presence

What to evaluate: How many reviews do you have on G2, Capterra, or the dominant review platform in your category? Are the reviews recent?

What good looks like: 50+ reviews on your primary review platform. Reviews are current (within the last 12 months). Your overall rating is strong (4.0+).

What bad looks like: Fewer than 20 reviews, or reviews that are 2+ years old. AI models weight review recency and volume.

Red flag: Your competitors have 200+ reviews and you have 30. This gap in third-party credibility directly affects how confidently AI models recommend you.

Fix: Build a systematic review generation process. Customer success team asks for reviews at key moments (after onboarding, at renewal). Make it easy - send a direct link to your review page.

5. Community Mention Quality

What to evaluate: Is your brand mentioned authentically in relevant community discussions - Reddit, Hacker News, Slack communities, industry forums? Are the mentions genuine and informative?

What good looks like: Your brand appears in relevant subreddit threads organically. Community members recommend you or discuss your product in substantive ways. These discussions exist without your active prompting.

What bad looks like: Your only community mentions are from your own marketing team. Mentions are thin ("check out [your brand]!") rather than substantive.

Red flag: Competitors are being discussed extensively in communities where your brand is rarely mentioned. AI models learn heavily from community content.

Fix: Engage authentically in relevant communities. Be genuinely helpful - answer questions, share expertise, contribute without always promoting. Build a community presence over time.

6. Schema Markup Implementation

What to evaluate: Do you have relevant structured data implemented on your site? At minimum: Organisation schema, Product schema, FAQ schema where applicable.

What good looks like: Organisation schema clearly states your company name, description, and product categories. Product pages have Product schema. FAQ sections use FAQ schema. HowTo schema on tutorial content.

What bad looks like: No schema markup, or only basic Open Graph tags.

Red flag: You have schema markup but it contains outdated information (wrong founding year, old product names, deprecated attributes). Outdated schema can actively hurt how models represent you.

Fix: Implement Organisation and Product schema first. Audit existing schema for accuracy. See Schema Markup for AI Search for implementation guidance.

7. llms.txt File and AI Crawler Configuration

What to evaluate: Do you have an llms.txt file at your domain root? Does your robots.txt accidentally block AI crawlers?

What good looks like: You have an llms.txt file with a clear site description, your key product pages listed, and content priority guidance for AI crawlers.

What bad looks like: No llms.txt file. Or, worse: your robots.txt blocks common AI crawler user agents, preventing models from reading your content at all.

Red flag: Your robots.txt disallows GPTBot, ClaudeBot, or PerplexityBot. Many sites do this accidentally when using wildcard bot blocks for performance reasons.

Fix: Check robots.txt immediately for AI crawler blocks. Create llms.txt following the standard format - see How to Write an llms.txt File.

8. Competitor Comparative Content

What to evaluate: Do you have content that addresses comparisons between you and your top 3-5 competitors? Comparison content performs particularly well in AI answers.

What good looks like: You have dedicated comparison pages ("Us vs. Competitor A") that are fair, specific, and genuinely useful. You also appear in third-party comparison articles.

What bad looks like: No comparison content. When someone asks ChatGPT "how does [your brand] compare to [competitor]?", the model has to guess from incomplete information.

Red flag: A competitor has published comparison content positioning themselves against you, and you have no counter-content. The model will weight that existing content.

Fix: Publish honest, specific comparison pages for your top 3 competitors. Address the actual trade-offs rather than just claiming you win everything.

9. Tracking and Iteration System

What to evaluate: Do you have a structured, recurring process for checking AI visibility and acting on changes?

What good looks like: Weekly or bi-weekly automated visibility checks across ChatGPT, Perplexity, Claude, and Gemini. A defined set of test queries. Trend tracking over at least 3 months. A clear owner who acts on changes.

What bad looks like: Occasional manual checks when someone asks about it. No tracking over time. No defined response process for visibility drops.

Red flag: You optimised 6 months ago and haven't checked results since. Model updates can shift visibility significantly - not tracking means not knowing.

Fix: Set up automated tracking. Assign a clear owner. Create a monthly review process. Bingly handles the automation - see Tracking & History for how recurring tracking works.

Scoring Your Audit

Work through each point and rate yourself: Strong / Needs Work / Not Done.

7-9 Strong: You're in the top tier of AI search optimisation. Focus on maintaining and expanding to new query areas.
4-6 Strong: Solid on the basics with meaningful gaps. Prioritise items 2 and 3 - entity clarity and use-case content are your highest-leverage fixes.
Under 4 Strong: Significant gaps across the board. Start with the baseline audit (item 1) and entity clarity (item 2). Everything else builds from there.

Track your AI visibility with Bingly - start free