Executive Summary: [BRAND_NAME], a [INDUSTRY] company, went from a [BEFORE_REC_RATE]% to [AFTER_REC_RATE]% AI recommendation rate in [TIMEFRAME] using friction AI's monitoring and optimization platform. Three key results: AI recommendation rate increased from [BEFORE_REC_RATE]% to [AFTER_REC_RATE]%, sentiment scores improved from [BEFORE_SENTIMENT] to [AFTER_SENTIMENT] across all tracked platforms, and citation rate grew from [BEFORE_CITATIONS] to [AFTER_CITATIONS] monthly citations in AI-generated answers.
The Challenge
[BRAND_NAME] had built a strong reputation through traditional channels. Their SEO performance was solid. Their review profiles were healthy. Their paid acquisition was delivering consistent returns. But a new problem was emerging.
When prospective customers asked AI assistants about their category, [BRAND_NAME] wasn't part of the conversation. Competitors were being recommended by ChatGPT and Perplexity while [BRAND_NAME] was absent or mentioned only as an afterthought.
The [CONTACT_NAME] team discovered this gap during a routine competitive analysis. They ran a set of category-related prompts through ChatGPT and found that [BRAND_NAME] appeared in fewer than [BEFORE_REC_RATE]% of recommendation-style queries. Meanwhile, two direct competitors appeared in over 60% of the same queries.
The specific challenges [BRAND_NAME] faced:
- Low visibility: AI platforms mentioned [BRAND_NAME] in less than [BEFORE_REC_RATE]% of relevant prompts
- Inconsistent brand representation: When mentioned, AI responses contained outdated descriptions of [BRAND_NAME]'s product capabilities
- Weak sentiment framing: Competitors were described with endorsement language ("widely recommended," "top choice") while [BRAND_NAME] received neutral or hedged framing ("another option," "also available")
- Minimal citations: [BRAND_NAME]'s content was rarely cited as a source, even on topics where they had published authoritative guides
The team recognized that this wasn't a temporary issue. AI-driven discovery was growing, and every month without action meant ceding more ground to competitors who were already showing up.
The Approach
[BRAND_NAME] partnered with friction AI to run a structured AI visibility improvement program over [TIMEFRAME]. The approach followed three phases.
Phase 1: Baseline Audit and Gap Analysis (Weeks 1-2)
friction AI's platform ran a comprehensive audit of [BRAND_NAME]'s AI presence across ChatGPT, Perplexity, Gemini, and Google AI Overviews. This established baseline metrics across five dimensions: visibility, accuracy, sentiment, citation rate, and recommendation rate.
The audit revealed specific, actionable gaps:
- [BRAND_NAME]'s Organization schema was incomplete, missing
sameAslinks,knowsAboutproperties, and a currentdescription - Their Wikipedia article hadn't been updated in over two years and contained outdated product information
- Review profiles on G2 and Capterra had strong ratings but low volume compared to competitors
- Key product pages lacked structured data markup entirely
- Several high-authority blog posts used thin content formats that AI systems struggled to cite
friction AI's competitive intelligence feature also mapped exactly where each competitor outperformed [BRAND_NAME] and why. This turned a vague problem ("we're not showing up in AI") into a specific, prioritized task list.
Phase 2: Entity and Content Optimization (Weeks 3-8)
Based on the audit findings, [BRAND_NAME] executed a structured optimization plan.
Entity signal strengthening. The team updated their Organization schema with complete properties, including knowsAbout, sameAs links to all social profiles and their Wikipedia page, and an accurate current description. They also updated their Wikidata entry and ensured consistent brand naming across every web property.
Content depth improvements. The team identified their 15 highest-priority topic areas (based on prompt coverage gaps found during the audit) and either created or expanded content for each. New content followed AI-optimized formats: clear question-answer structures, original data where available, and comprehensive coverage of each subtopic.
Review profile expansion. [BRAND_NAME] launched a systematic review collection program targeting G2 and Capterra. They focused on collecting detailed reviews that mentioned specific product features and use cases, since reviews with specific detail provide richer training data signals than generic positive reviews.
Technical markup deployment. The team added Article schema to all blog content, Product schema to product pages, and FAQ schema to their help center. Every schema implementation was validated through Google's Rich Results Test and Schema.org's validator.
Phase 3: Monitoring and Iteration (Ongoing)
friction AI's continuous monitoring tracked the impact of each optimization in near-real-time. The team held bi-weekly reviews of their AI visibility metrics, adjusting priorities based on what was moving the needle.
When certain content changes produced measurable citation improvements within days on Perplexity (which uses live web retrieval), those patterns were applied across other content areas. When entity signal improvements took longer to surface in ChatGPT's responses (expected, given training data cycles), the team maintained patience and continued executing.
The Results
The results accumulated over [TIMEFRAME], with the most dramatic improvements appearing after entity signal changes had time to propagate across AI platforms.
Before and After Comparison
| Metric | Before | After | Change |
|---|---|---|---|
| AI Recommendation Rate | [BEFORE_REC_RATE]% | [AFTER_REC_RATE]% | +[AFTER_REC_RATE] - [BEFORE_REC_RATE] percentage points |
| Sentiment Score | [BEFORE_SENTIMENT] | [AFTER_SENTIMENT] | Improved |
| Monthly AI Citations | [BEFORE_CITATIONS] | [AFTER_CITATIONS] | Increased |
| ChatGPT Visibility | Low | Consistent top-3 mention | Significant improvement |
| Perplexity Citations | Rare | Regular source citations | Significant improvement |
| Gemini Recommendations | Absent | Present in category queries | New presence established |
Platform-by-Platform Breakdown
ChatGPT. [BRAND_NAME] moved from sporadic mentions to consistent top-three positioning in category recommendation prompts. The most impactful factor was the combination of updated Wikipedia content and expanded G2 review volume. These sources carry significant weight in ChatGPT's training data.
Perplexity. Results appeared fastest on Perplexity, because it uses live web retrieval rather than static training data. Within two weeks of deploying improved content with proper schema markup, [BRAND_NAME]'s pages began appearing as cited sources. Citation rate grew steadily as more optimized content was published.
Gemini. [BRAND_NAME] had been completely absent from Gemini's recommendations at baseline. By the end of [TIMEFRAME], they appeared in category queries consistently. Google Knowledge Graph entity improvements (driven by Organization schema and Wikidata updates) were the primary driver.
Google AI Overviews. Citation rate in AI Overviews improved alongside traditional SEO gains from the content depth improvements. [BRAND_NAME]'s content was cited in AI Overviews for 8 of their top 15 target queries, up from 2 at baseline.
What [CONTACT_NAME] Said
"[QUOTE]"
Key Takeaways
These lessons from [BRAND_NAME]'s experience apply to any brand working to improve AI visibility.
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Entity signals are foundational. Schema markup, Wikipedia presence, Wikidata entries, and consistent brand naming across the web form the base layer of AI visibility. [BRAND_NAME]'s biggest early wins came from fixing these foundational elements, not from content creation.
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Review volume matters more than review score. [BRAND_NAME]'s review ratings were already strong before the program. What moved the needle was increasing review volume and depth on platforms that contribute to LLM training data. A thousand detailed reviews create stronger entity signals than a hundred brief ones.
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AI visibility compounds over time. The first few weeks showed modest improvements. The most significant gains came after multiple optimization layers (entity signals, content depth, review volume, structured data) reinforced each other. Brands that expect overnight results will be disappointed. Brands that commit to sustained optimization will pull ahead.
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Continuous monitoring changes the game. Without friction AI's ongoing tracking, [BRAND_NAME] would have been flying blind. Knowing which changes produced results (and which didn't) allowed the team to allocate resources efficiently. The brands making the fastest progress are the ones measuring AI visibility as a continuous metric.
Start Your AI Visibility Journey
[BRAND_NAME]'s results aren't unique to their industry or size. The optimization playbook, strengthening entity signals, deepening content, expanding review profiles, and deploying structured data, works across categories.
The first step is understanding where you stand today. friction AI runs a comprehensive AI brand health audit that shows your visibility, sentiment, recommendation rate, and competitive positioning across every major AI platform.
Whether you're starting from zero AI presence or looking to extend an existing lead, the path forward starts with measurement.
See pricing and start your AI visibility program →