AI Visibility & Social Proof Playbook

This playbook is designed to be client-agnostic — a repeatable framework that any business can apply to strengthen brand presence in Large Language Models (LLMs) and AI systems. It combines social proof strategy with LLM.txt best practices.

1. Why AI Visibility Matters

• AI-driven search is rapidly replacing traditional search engines. Tools like ChatGPT, Claude, Perplexity, and Gemini increasingly answer questions directly.

• Entity visibility in LLMs determines whether a business is mentioned, recommended, or overlooked in these answers.

• Social proof (reviews, testimonials, case studies) and structured signals (sitemaps, schema, LLM.txt) are two of the strongest levers to increase visibility.

2. Core Principles of AI Visibility

1. Entity Recognition: Ensure the business is consistently represented across web, schema, and external authority sources (Wikipedia, LinkedIn, review sites).

2. Content Exposure: AI crawlers should see core content without needing JavaScript — focus on static HTML availability.

3. Knowledge Prioritisation: FAQs, sustainability, certifications, and customer support content are high-value for AI training.

4. Authority Signals: External reviews, third-party mentions, and partner links validate the entity.

3. Role of Social Proof

Social proof is critical because LLMs:

• Scrape reviews and ratings to form brand sentiment.

• Use testimonials and case studies to answer user intent (e.g., “best supplier for X”).

• Triangulate trust signals from multiple platforms.

Types of Social Proof to Prioritise

• Customer Reviews: Google Business Profile, Trustpilot, Yelp, G2, Capterra.

• Social Mentions: Twitter/X, Instagram, TikTok, LinkedIn posts.

• Case Studies: Documented success stories in crawlable HTML.

• Certifications/Badges: ISO, sustainability labels, industry accreditations.

• Press Quotes: Independent media references.

4. Social Proof Implementation Framework

1. Centralised Reviews Hub

   • Create a dedicated /reviews page.

   • Combine curated static reviews (crawlable HTML) + live feeds (server-side rendered APIs).

   • Add structured data (Review, AggregateRating).

2. Branch / Location Reviews

   • Aggregate branch-level Google reviews.

   • Roll up into an “average by branch” table for LLM clarity.

3. Social Content Integration

   • Showcase real projects (UGC gallery).

   • Include crawlable captions + links to original posts.

4. Certifications Section

   • Logos + descriptions of certifications.

   • Link to sustainability and compliance pages.

5. Case Studies & Testimonials

   • Present as static articles with schema markup.

   • Ensure press quotes and customer names are crawlable.

6. FAQs for Social Proof

   • Address review sources, update frequency, submission process.

5. LLM.txt Strategy

The LLM.txt file acts as a crawler guide for AI models. It:

• Sits at domain.com/llm.txt (or llms.txt).

• Functions like robots.txt but tailored for AI entity ingestion.

Recommended Structure

Key Guidelines

• Keep URLs direct and explicit — avoid vague titles.

• Prioritise knowledge pages and social proof hubs high in the list.

• Link to external authorities (Wikipedia, LinkedIn, reviews platforms) to reinforce entity credibility.

• Use crawl-delay to signal responsible bot behaviour.

6. Measurement & Monitoring

1. AI Mentions Tracking

   • Query ChatGPT, Claude, Perplexity monthly for brand inclusion.

   • Track competitors mentioned alongside.

2. Review Score Monitoring

   • Benchmark review averages (Google, Trustpilot).

   • Ensure schema aggregate ratings match third-party data.

3. Schema Validation

   • Use tools like Google Rich Results Test for Review and AggregateRating.

4. Log Analysis

   • Track crawler hits to /llm.txt, /reviews, /faqs.

7. Ongoing Optimisation

• Refresh Reviews: Update static snapshots weekly.

• Add New Sources: When new mentions or press hits occur, link them in llm.txt.

• Iterate FAQs: Expand based on common customer/AI queries.

• E-A-T Alignment: Ensure reviews and testimonials highlight expertise, authority, and trustworthiness.

8. Quick Checklist

🔹 Social Proof

• /reviews page live, crawlable, with Review + AggregateRating schema

• Curated static reviews included + server-side rendered live feeds

• Branch/location reviews aggregated (ratings + sample comments)

• Social proof gallery with UGC photos + crawlable captions

• Certifications, badges, and partner logos with descriptive text

• Case studies & testimonials in static HTML with schema

• FAQs explaining review sources, update frequency, and submission process

🔹 LLM.txt

• File live at /llm.txt (or /llms.txt) at the root domain

• Includes sitemap + robots.txt references

• References to company overview, contact, reviews, sustainability, FAQs

• Explicit URLs for product/service categories

• Links to /schema/organization.json, /schema/product.json, /schema/faq.json

• External authority references (Wikipedia, LinkedIn, Trustpilot, socials)

• Crawl-delay directive (e.g., Crawl-delay: 1)

🔹 Schema & Metadata

• /schema/organization.json live with entity, sameAs, contact points

• /schema/product.json summarising categories or product feed

• /schema/faq.json aligned with visible FAQs

• Organization schema includes AggregateRating

🔹 Monitoring & Optimisation

• Track brand mentions in AI (ChatGPT, Claude, Perplexity) monthly

• Monitor review averages across platforms (Google, Trustpilot, etc.)

• Validate schema with Google Rich Results Test

• Log crawler hits to /llm.txt and /reviews

• Refresh review snapshots weekly, FAQs regularly

• Update LLM.txt with new press mentions or authority links
  

Final Note

AI visibility is not accidental. It’s engineered through:

• Crawlable, structured social proof.

• Smart, comprehensive LLM.txt.

• Continuous refresh of signals and authority sources.

Businesses that systemise these steps will consistently surface in AI-driven answers, ahead of competitors who neglect them.