Stanley 1913 is reworking how it describes its products online so they surface in AI chatbot answers - a shift the brand says is about structure, not gaming the system.
The lifestyle brand, known for its tumblers and drinkware, has started adding product-level FAQs, care instructions, and usage guides built around the kinds of questions people ask AI engines: gifting, hydration, fitness, travel, hosting. The goal is to give large language models the detailed text they need to cite the brand in responses.
"It's about ensuring our authentic brand experiences translate into natural-language answers without losing the human touch," said chief brand officer Kate Ridley.
From visual storytelling to written detail
The gap became obvious around occasion-based marketing. Stanley 1913 had built plenty of visual content around Mother's Day, Teacher Appreciation Week, and Nurse Appreciation - but it was heavy on imagery, light on explanation.
"We weren't necessarily writing copy that was really detailing the use, the occasion and why it was a great gift option," Ridley said. "Now we recognize that's actually really important for LLMs."
The timing matters. More than four in 10 U.S. adults (42%) now use AI chatbots to search for information, according to Pew Research Center. Stanley 1913's marketing has done real work on brand awareness through influencer-led visual storytelling, but that content was built for humans, who can infer meaning from an image or a trusted face. LLMs aggregate what's already on the open web - without underlying detail, a brand won't surface no matter how strong its social presence is.
Stanley 1913's website received 6.6 million global desktop and mobile visits in July 2026, a 35.5% increase compared to July 2025, according to Similarweb data. As that audience grows, so does the number of platforms where consumers can discover the brand.
A cross-functional effort
Stanley 1913 isn't treating this as an SEO side project. It's a cross-functional effort spanning content, SEO, e-commerce, technology, PR, and marketing - reflecting the fact that AI platforms pull from product data, brand storytelling, and third-party sources alike. Ridley said the brand is "actively building out dedicated LLM guidance and measurement frameworks so everyone stays aligned."
On the infrastructure side, Stanley 1913 has been testing Shopify and Google's Universal Commerce Protocol to bring its product catalog directly into chat conversations, and using structured data to keep product information consistent for both traditional search and AI agents. Working with partners including Yotpo's Discovery product, the brand is building a baseline for how often it appears in LLM conversations and how frequently its products get cited.
For marketing professionals, the playbook here is worth watching: Stanley 1913 built its brand through human creators, and now it's treating AI platforms as a different kind of influencer. AI for Marketing training increasingly covers this exact transition - moving from visual storytelling to structured, query-ready content. The brand is also tracking which third-party sources agentic search tools treat as authorities and directing its PR efforts accordingly. Culturally-led campaigns, like the recent Kacey Musgraves partnership, feed into that by generating press coverage beyond the brand's own channels.
Debra Aho Williamson, founder and chief analyst at Sonata Insights, put it directly: "Stanley did an incredible job of reintroducing its brand to consumers via human creators and influencers. Now, brands like Stanley are recognizing that they need to think of AI platforms as influencers of a different type. They are becoming important places where consumers discover new brands, learn about them and then make purchase decisions - the same roles human influencers play."
None of this replaces what Stanley 1913 was already doing. "We're not moving away from one and towards the other," Ridley said, talking about social versus AI platforms. "We need to do both to make sure we're providing as much detailed information about our products as possible." For LLMs specifically, "you have to have the information there for it to do its job."
Why this matters for marketers
The takeaway for marketing teams is concrete: visual brand content and AI-search visibility require different assets. If your product pages rely on imagery and vibe, LLMs can't cite them. The fix isn't abandoning influencer marketing - it's pairing it with written detail that answers the questions people actually ask chatbots. That means product-level FAQs, occasion-based copy, and structured data, coordinated across content, SEO, and PR teams.
For those building these capabilities, AI for SEO Specialists training covers the technical side - structured data, citation tracking, and optimizing for LLM answers rather than just traditional rankings. Stanley 1913's approach shows the payoff: a 35.5% traffic jump in a year, with AI platforms becoming a measurable discovery channel alongside social.
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