How We Increased a Brand's AI Mentions by 340% in 90 Days

When Meridian Home came to us in late 2025, they had a problem that an increasing number of mid-market brands face: they were effectively invisible to AI search engines. Despite selling premium, well-reviewed home goods through their DTC channel, they were almost never mentioned when consumers asked ChatGPT, Perplexity, Claude, or Gemini for product recommendations in their category.

This is the detailed story of how we turned that around, achieving a 340% increase in AI brand mentions, a 67% recommendation rate in relevant queries, and a 28% lift in organic traffic from AI-referred visitors, all within 90 days.

About Meridian Home

Meridian Home is a mid-market direct-to-consumer brand specializing in premium home goods, including handcrafted ceramics, sustainably sourced textiles, and curated kitchen accessories. Founded in 2019, they had built a loyal customer base through Instagram marketing, influencer partnerships, and strong word-of-mouth.

Key brand stats at engagement start:

• Annual revenue: $12M
• Product catalog: ~350 SKUs
• Average order value: $85
• Customer satisfaction rating: 4.7/5 across review platforms
• Primary marketing channels: Instagram, Facebook Ads, Google Shopping

By any traditional measure, Meridian Home was a healthy, growing brand. But they were missing an entirely new discovery channel that was rapidly growing in importance.

The Challenge: Invisible to AI

Meridian Home's CEO reached out to us after a jarring realization. She had asked ChatGPT, "What are the best DTC home goods brands for premium ceramics?" and Meridian Home was nowhere in the response. Instead, the AI recommended three competitors and two brands she had never even heard of.

When we ran our initial AI visibility audit, the numbers confirmed her concern.

Baseline AI Visibility Metrics

Meridian Home was being mentioned in only 8% of relevant AI queries across ChatGPT, Perplexity, Gemini, and Claude. When they were mentioned, it was typically as an afterthought at the bottom of a recommendation list. Their competitors, meanwhile, were dominating the conversation.

Why Competitors Were Winning

Our competitive analysis revealed several reasons why competitors were outperforming Meridian Home in AI recommendations:

• Structured content: Competitors had well-organized product pages with clear category hierarchies, feature comparisons, and use-case descriptions that AI models could easily parse.
• Third-party authority: Top competitors were mentioned in 15-25 editorial reviews, gift guides, and comparison articles on high-authority domains. Meridian Home appeared in only 3.
• Review ecosystem: Competitors had reviews syndicated across multiple platforms (Google, Trustpilot, industry-specific sites), while Meridian Home's reviews lived almost exclusively on their own website.
• Technical foundation: Competitors used robust structured data markup (Product schema, Review schema, FAQ schema) that made their data machine-readable. Meridian Home had minimal schema implementation.
• Brand entity recognition: Competitors had established "brand entities" that AI models recognized as authorities in specific product categories. Meridian Home had no clear entity associations.

The gap was significant, but not insurmountable. Meridian Home had the product quality, customer satisfaction, and brand story to compete. They just needed the right strategy to make those strengths visible to AI models.

The Audit: What We Found

Before building a strategy, we conducted a comprehensive AI visibility audit across four dimensions.

1. Content Architecture Audit

We analyzed every page on meridian-home.com (287 pages total) and scored them on AI readability:

• 72% of product pages lacked structured descriptions that would help AI models understand product positioning
• Zero comparison content existed (no "Meridian Home vs. [competitor]" or "best [category]" pages)
• Blog content was exclusively lifestyle-focused (beautiful photography, minimal searchable text) with no educational or comparison content
• Category pages had thin content with no contextual information about why Meridian Home's approach to each category was differentiated
• FAQ content was limited to shipping and returns, missing product-focused questions that AI models frequently draw from

2. Authority Signal Audit

We mapped Meridian Home's presence across the web:

• 3 editorial mentions on mid-tier publications (compared to 18-25 for top competitors)
• No Wikipedia presence or entries on curated industry databases
• Limited backlink profile from authoritative home goods review sites
• Strong Instagram presence (85K followers) but minimal content on platforms AI models index effectively
• No presence on key product comparison sites or curated recommendation lists

3. Review Ecosystem Audit

We assessed Meridian Home's review footprint:

• 1,247 reviews on their own site (strong)
• 12 reviews on Google Business Profile (weak)
• 0 reviews on Trustpilot, ConsumerAffairs, or industry-specific platforms
• 23 reviews on Amazon (they had recently launched a limited Amazon presence)
• No structured review data being surfaced in a machine-readable format

4. Technical SEO & Schema Audit

We evaluated the technical foundation:

• Basic Product schema on 40% of product pages (incomplete)
• No Review/AggregateRating schema despite having strong reviews
• No FAQ schema anywhere on the site
• No Organization schema on the homepage
• No BreadcrumbList schema for navigation structure
• Missing HowTo schema on their care guides and usage content

The audit painted a clear picture: Meridian Home had excellent products and happy customers, but they had built their digital presence for humans browsing Instagram, not for AI models synthesizing recommendations.

The Strategy: Our 6-Step Approach

Based on the audit findings, we developed a 90-day strategy with six interconnected workstreams. Each was designed to address a specific gap identified in the audit.

Step 1: Content Restructuring (Weeks 1-4)

The foundation of our approach was rebuilding Meridian Home's content architecture to be AI-parseable while remaining compelling for human visitors.

What we did:

• Rewrote 87 product descriptions to include structured benefit statements, use-case contexts, material specifications, and comparison-friendly language
• Created 12 category pillar pages that positioned Meridian Home's expertise in each product category (e.g., "Handcrafted Ceramics: Our Approach to Artisan Pottery")
• Built 8 comparison pages addressing head-to-head queries (e.g., "Meridian Home vs. [Competitor]: Premium Home Goods Compared")
• Developed 15 "Best of" content pieces targeting common AI query patterns (e.g., "Best Sustainable Kitchen Accessories for Small Apartments")
• Added comprehensive FAQ sections to every category page and top 50 product pages, with questions modeled on actual AI query patterns

Key principle: Every piece of content was structured with clear headers (H2/H3), concise summary statements, bullet-pointed feature lists, and definitive category claims. AI models favor content that makes clear, structured assertions over content that is purely narrative or aesthetic.

Step 2: Authority Signal Building (Weeks 2-8)

AI models weight third-party mentions heavily when deciding which brands to recommend. We needed to build Meridian Home's presence on authoritative external sites.

What we did:

• Secured placements in 14 editorial roundups on home goods publications (Apartment Therapy, The Strategist, Wirecutter's secondary lists, Design Milk, and others)
• Placed 6 guest bylines from Meridian Home's founder on topics like sustainable sourcing, artisan manufacturing, and home design trends
• Contributed to 4 expert roundup articles on interior design and sustainable living publications
• Created a brand page on 3 industry databases (home goods directories, sustainable brand registries, DTC brand indexes)
• Updated and expanded Meridian Home's Crunchbase profile with accurate company data, funding information, and product descriptions

Key insight: We discovered that AI models heavily reference a relatively small set of "authority nodes": major review sites, curated lists, and industry databases. Getting onto these specific sources had an outsized impact compared to general PR efforts.

Step 3: Review Optimization (Weeks 3-8)

Reviews are one of the strongest signals AI models use when generating product recommendations. We needed to distribute Meridian Home's strong customer sentiment across the review ecosystem.

What we did:

• Launched a post-purchase review solicitation flow that directed happy customers to leave reviews on Google, Trustpilot, and category-specific platforms (not just the brand's own site)
• Claimed and optimized profiles on Trustpilot, ConsumerAffairs, and Product Hunt
• Responded to every existing review across all platforms with thoughtful, brand-consistent responses
• Created a review highlights page on the website with structured AggregateRating schema
• Implemented review syndication from Shopify to Google Shopping and merchant feeds

Results by Day 90:

Step 4: Structured Data Implementation (Weeks 2-5)

We implemented comprehensive schema markup to ensure AI models could machine-read Meridian Home's data with maximum fidelity.

Schema types implemented:

• Product schema on all 350 product pages (with offers, pricing, availability, brand, category)
• AggregateRating schema connected to review data across platforms
• FAQ schema on 65 pages (category pages, top products, about page)
• Organization schema on homepage with founding date, description, social profiles, and contact information
• BreadcrumbList schema across all navigation paths
• HowTo schema on 18 care guide and usage pages
• Review schema on the reviews highlights page
• Article schema on all blog content

Technical detail: We also implemented JSON-LD sitelinks search box schema and speakable schema on key pages, anticipating voice-based AI query patterns. While not yet widely adopted, these forward-looking implementations position Meridian Home for emerging AI interaction modes.

Step 5: Third-Party Mention Cultivation (Weeks 4-12)

Beyond editorial placements, we focused on building the broader web of third-party mentions that AI models use to validate brand authority.

What we did:

• Partnered with 8 home design bloggers for authentic product reviews (not sponsored posts, genuine reviews with full editorial independence)
• Submitted Meridian Home to 12 "best of" lists curated by design publications and influencer roundups
• Created a press kit and media resource page optimized for journalist and AI discoverability
• Launched a design professional partnership program that generated mentions on interior designer websites and portfolios
• Contributed product data to comparison engines and shopping aggregators that AI models frequently cite

Key tactic: We identified the specific sources that each major AI model cited most frequently in the home goods category by running hundreds of test queries. Then we prioritized getting Meridian Home mentioned on those exact sources. This targeted approach was far more efficient than generic PR outreach.

Step 6: Ongoing Monitoring & Optimization (Weeks 1-12, Ongoing)

Throughout the engagement, we ran continuous AI visibility monitoring to track progress and adjust tactics.

Monitoring methodology:

• Weekly query testing: We ran 150+ standardized queries across ChatGPT, Perplexity, Gemini, and Claude to track mention rates, position, and sentiment
• Competitor benchmarking: Same query set run for 5 key competitors to track relative positioning
• Traffic attribution: Custom UTM parameters and referral tracking for AI-originated traffic
• Conversion tracking: Post-purchase survey added "How did you hear about us?" with AI platform options
• Content performance: Page-level analytics on new and restructured content to identify top performers

We adjusted strategy in real time based on monitoring data. For example, when we noticed that Perplexity was indexing comparison pages faster than other AI models, we accelerated the creation of comparison content in weeks 5-6.

The Results: 90-Day Outcomes

Month 1 (Days 1-30): Foundation Building

The first month was primarily about building the foundation. Results were modest but directional:

• AI mention rate increased from 8% to 14% (+75%)
• Structured data implementation completed across all product pages
• 42 product descriptions rewritten and published
• 4 comparison pages and 6 "Best of" pages live
• First editorial placements secured (3 articles published)

Key learning from Month 1: The fastest wins came from structured data implementation. Simply adding proper Product and FAQ schema led to a noticeable uptick in Perplexity citations within 2 weeks.

Month 2 (Days 31-60): Acceleration

Month 2 was the inflection point. Third-party mentions began compounding with on-site improvements:

• AI mention rate increased from 14% to 26% (+86% from Month 1)
• First instances of Meridian Home appearing as a top-3 recommendation
• Review count across external platforms crossed 200
• 14 editorial placements published
• AI-referred traffic grew from ~120 to ~340 visits/month

Key learning from Month 2: We saw a clear "tipping point" effect. Once Meridian Home appeared on 8+ authoritative external sources, AI models began mentioning the brand much more frequently, even in queries we hadn't specifically optimized for. The authority signals had a generalization effect.

Month 3 (Days 61-90): Dominance in Category

Month 3 delivered the headline results:

• AI mention rate reached 35.2% (up from 8% baseline = 340% increase)
• Recommendation rate in relevant queries: 67% (mentioned as a recommended brand)
• Average recommendation position: 2.1 (up from 4.5)
• AI-referred traffic: ~465 visits/month (28% increase from baseline of ~363 total organic visits from AI sources)
• Share of voice across AI platforms: 18.4% (up from 3.2%)

Comprehensive Results Summary

Platform-by-Platform Breakdown

Not all AI platforms responded equally. Here is how Meridian Home's visibility changed across each:

Revenue Impact

While AI visibility is still an emerging attribution channel, we tracked several revenue-relevant metrics:

• AI-referred visitors converted at 4.2% (vs. 2.1% site-wide average) due to higher purchase intent
• Average order value from AI-referred visitors was $112 (vs. $85 site average), suggesting AI recommendations attract premium-seeking buyers
• Post-purchase survey data: 8.3% of new customers in Month 3 reported discovering Meridian Home through an AI assistant (up from 1.1% at baseline)
• Estimated monthly revenue from AI-referred traffic: ~$22,000 (Month 3) vs. ~$5,300 (baseline)

What Surprised Us

Every engagement teaches us something new. Here are the findings from the Meridian Home case study that were unexpected or counterintuitive.

1. Instagram Authority Did Not Transfer to AI

Meridian Home had 85K Instagram followers and strong social proof. We expected this to carry some weight with AI models. It did not. AI models overwhelmingly draw from text-based, indexable web content. A brand can be dominant on social media and completely invisible to AI search. This disconnect is one of the biggest blind spots for DTC brands.

2. The "Tipping Point" Was Real and Sudden

Between Day 38 and Day 45, Meridian Home's mention rate nearly doubled with no specific tactical change during that window. Our analysis suggests that reaching a critical mass of third-party mentions (we estimate 8-10 authoritative sources) triggers a rapid revaluation by AI models. The improvement was not linear; it was exponential once the threshold was crossed.

3. Perplexity Was the Fastest Mover

Perplexity reflected content and authority changes significantly faster than other AI platforms. Changes that took ChatGPT 4-6 weeks to incorporate were visible on Perplexity within 7-10 days. This makes Perplexity an excellent "early indicator" platform for AI visibility efforts.

4. Comparison Content Had Outsized Impact

The 8 comparison pages we created accounted for only 2.8% of Meridian Home's total pages but were responsible for an estimated 23% of new AI mentions. AI models frequently reference comparison content when generating recommendations, making this format disproportionately valuable.

5. Review Distribution Mattered More Than Review Volume

Meridian Home already had 1,247 reviews on their own site. But adding even modest review counts on Trustpilot (134) and Google (89) had a larger impact on AI mentions than the existing 1,247 on-site reviews. AI models appear to weight review diversity (multiple independent platforms) over review volume on a single source.

Key Learnings for Other Brands

Based on the Meridian Home engagement and our broader portfolio of AI visibility work, here are the most important takeaways.

For DTC and E-commerce Brands

1. Your Instagram following is invisible to AI. Invest in creating indexable, text-rich content that AI models can parse. Beautiful product photography alone will not earn AI recommendations.

2. Comparison content is non-negotiable. If you are not creating head-to-head comparisons with competitors, you are ceding the recommendation conversation to others. AI models need comparative context to position your brand.

3. Distribute your reviews. A single review platform, even with thousands of reviews, signals less authority to AI than moderate review counts across multiple independent platforms.

4. Structured data is the table stakes. Comprehensive schema markup is not a competitive advantage; it is a prerequisite. Without it, AI models may not correctly associate your brand with your product categories.

5. Start now. AI search is growing at 30-40% quarter over quarter. Every month you delay optimization, competitors are building authority that becomes harder to displace. Early movers have a compounding advantage.

For Marketing Leaders and CMOs

1. Budget for AI visibility as a distinct channel. It overlaps with SEO but requires different tactics, metrics, and expertise. Treating it as an add-on to your SEO program will produce mediocre results.

2. Measure what matters. AI mention rate, share of voice, recommendation position, and AI-referred traffic should be on your marketing dashboard alongside traditional metrics.

3. Expect a 60-90 day ramp. AI visibility is not a quick fix. Plan for a 90-day initial engagement with ongoing optimization. The brands that commit to sustained effort see compounding returns.

4. Your competitors are already doing this. In every category we audit, at least one brand has already begun optimizing for AI visibility. The question is whether you want to catch up or fall further behind.

What Happened After Day 90

Meridian Home continued AI visibility optimization beyond the initial 90-day engagement. At the six-month mark, their metrics had continued to improve:

• AI mention rate: 47% (up from 35.2% at Day 90)
• Share of voice: 24% (approaching the previous category leader)
• AI-referred monthly traffic: ~780 visits
• Estimated monthly revenue from AI referrals: ~$38,000

The compounding effect of sustained AI visibility investment is one of the most compelling aspects of this channel. Unlike paid advertising, where traffic stops when spend stops, AI visibility improvements tend to persist and grow as long as content and authority signals are maintained.

Ready to Increase Your Brand's AI Visibility?

If your brand is in a similar position to where Meridian Home started, invisible to AI despite having strong products and customer satisfaction, the playbook in this case study can serve as a starting framework.

However, every brand's competitive landscape, content foundation, and category dynamics are different. The specific tactics, priorities, and timelines need to be customized.

At AdsX, we help brands audit their current AI visibility, develop a customized optimization strategy, and execute across all the workstreams described in this case study. Our team has worked with dozens of brands across e-commerce, SaaS, professional services, and other categories to build measurable AI search presence.

Get a free AI visibility audit to see where your brand stands and what a 90-day optimization plan could look like for your specific situation.