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- Why AI search changes what brands must say
- Rewriting product language for real queries
- Organizational shifts: not an SEO side project
- Technical moves to make product data discoverable
- Partner tools and measurement
- Leveraging earned media and cultural moments
- What changed about occasion-driven marketing
- Balancing human storytelling with machine-readable detail
- Tracking authority and adjusting distribution
- Scale and traffic context
- Ongoing experiments and next steps
Stanley 1913 is quietly remapping how it talks about products for a world where AI does the searching. Rather than chasing quick algorithmic hacks, the brand has shifted to methodical work: rewriting product copy, reorganizing teams and feeding machines the explicit details LLMs need to recommend products to shoppers.
Why AI search changes what brands must say
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Language models treat the web like a giant encyclopedia. Images and vibes that win on social media often do not carry the factual signals these models require. As a result, brands with striking visual campaigns can still be invisible to AI agents.
More than four in 10 U.S. adults — 42% — use AI chatbots to find information, according to Pew Research Center. That shift means discovery is happening inside chat windows and assistant replies, not just on search results or social feeds.
Stanley 1913 found that its image-forward approach excelled at brand awareness. But LLMs need explicit, structured product details to cite a brand as a recommended option.
Rewriting product language for real queries
The brand began by mapping the actual questions people ask before a purchase. The result is a library of product-level content built for conversational search.
- New FAQs that answer common buyer concerns.
- Care and maintenance instructions for longevity and trust.
- Usage guides tailored to occasions — gifting, travel, fitness and hosting.
- Copy that links features to human benefits in plain language.
Kate Ridley, chief brand officer, framed the work as translating brand moments into natural-language answers while keeping the human warmth intact. The change is less about tone and more about clarity.
Organizational shifts: not an SEO side project
Stanley treats AI search as an enterprise-level priority. The effort spans content, SEO, e-commerce, tech, PR and marketing.
That cross-functional alignment matters because AI platforms pull from product data, brand storytelling and third-party sources at once.
The company has set up LLM guidance and measurement frameworks. Teams share playbooks so product attributes, campaign storytelling and earned media all feed the same data layer.
Technical moves to make product data discoverable
On the technical front, the brand is testing ways to pipe catalog data directly into conversational platforms.
Product feeds and protocols
- Trials with Shopify to standardize product feeds.
- Exploration of Google’s Universal Commerce Protocol to enable product mentions inside chat interfaces.
- Use of structured data to keep details consistent between traditional search and AI agents.
These steps reduce the chance that different sources report conflicting specifications. When a model pulls information that is consistent across the web, the brand is more likely to be surfaced.
Partner tools and measurement
Stanley is working with third-party vendors to benchmark its visibility in LLM-driven conversations.
- Yotpo’s Discovery product helps track how often Stanley appears in AI-generated recommendations.
- Analytics tie product-level mentions back to PR, influencer and content activity.
This measurement gives the team signals about which types of content actually cause an assistant to cite a product.
Leveraging earned media and cultural moments
LLMs value authoritative outside sources. That pushes brands to earn mentions beyond owned channels.
Stanley directs effort toward outlets and affiliates that AI agents deem trustworthy. High-profile partnerships and campaigns create the kind of coverage that gets crawled and cited.
For example, collaborations with artists and creators generate press and social chatter. That chatter becomes data LLMs can use to validate a product’s relevance for occasions like gifting or travel.
What changed about occasion-driven marketing
The brand had rich visual assets for holidays and appreciation weeks. But LLMs needed concrete reasons to recommend a product as a gift for a specific occasion.
So the team supplemented imagery with short, descriptive copy that explains use cases and why a product fits an occasion. Those additions are small, but they directly feed the kind of signals conversational AI relies on.
Balancing human storytelling with machine-readable detail
Stanley isn’t abandoning creator-led storytelling. Social and influencer work still drives awareness and cultural relevance.
The difference is layering: keep the evocative visuals, and add machine-friendly facts underneath. That dual approach preserves emotional appeal while improving discoverability in AI-driven search.
Tracking authority and adjusting distribution
Because LLMs synthesize multiple sources, the brand continually audits which third-party outlets act as authorities.
- Monitor earned media and reviews for consistency.
- Prioritize partnerships that generate credible outside coverage.
- Feed consistent metadata and structured product specs to partners and platforms.
Analysts note the shift is similar to treating AI platforms as a new class of influencers. They can introduce consumers to brands and shape buying decisions.
Scale and traffic context
The stakes are measurable. Stanley’s website drew 6.6 million visits in July 2026, about a 35.5% increase year over year, per Similarweb data. As online audiences grow, discovery pathways multiply.
That expansion makes product information mobility more important. A single clear spec can ripple across search engines, marketplaces and conversational agents.
Ongoing experiments and next steps
The brand continues to test feed-based integrations and structured markup to improve citation rates. It also builds internal metrics that show how often products are recommended by LLMs.
Work remains iterative: tune content, measure mentions, refine sources and repeat. The aim is to make sure the human story and the machine-readable facts reinforce one another so Stanley’s products surface when consumers ask AI agents for guidance.
