How will AI find local stores?
There has been a lot of discussion about agents, bots, self-driving vehicles (Tesla Robotaxi), and personal AI assistants (Meta’s Muse) lately. While operating two gas stations with extensive convenience stores, the one question that began to stick with me was “How will people and their automations find us in the near future, and how will the automated vehicle know where to go for specific products?” I couldn’t find a great answer to that question, because for the most part, there isn’t much of a solution yet for brick-and-mortar retailers.
Most non-enterprise businesses rely on outdated websites and a claimed Google Maps listing, which works well enough for the world and internet as we knew it, but will leave a sizeable gap to be filled once the way people search and make decisions shifts. You can already see this with a simple Google search; the classic “blue link” results get pushed down, and covered by the AI search mode, which immediately begins recommending products, services, and where the user should consider going. Additionally, people now turn straight to their favorite AI chatbot: Google’s Gemini, ChatGPT, Perplexity, Claude and more. Getting listed is one thing. Getting listed with fresh, relevant information is a whole separate problem, and it’s a problem that most small and medium-sized businesses don’t solve with their standard website.
Do you need a new website? Potentially, but updating a full website regularly with key products, services, promotions, amenities, etc. is a chore and expensive if someone else is managing it for you. And even if you were a vigilant updater, getting long lists of shelf inventory, pricing and other product information onto the site would take hours if not days. It’s not realistic barring a team that does only that, which is where most enterprise companies land. So how would a small retailer compete? How could they quickly, and with low friction, update the contents of their store?
Pictures. With the technology today, most shelf data can be pulled from a picture: what products are on the shelf, what price they’re sold for (assuming there is a tag or signage). It can pull amenities, equipment, signage, and essentially anything captured in that picture. Pictures also provide freshness which the agents, bots and AI platforms tend to trust, because it gives the system more confidence to reference a store or product list.
Separately, how do I know where I stand in the new ecosystem? How many times has a user been sent to me via an AI platform, or another specific agent or bot? This is another metric that can’t be easily measured yet for small businesses. This is getting handled for the ecommerce crowd, but to get referenced or recommended for what is in a store is largely unanswered. This is why I created StreetTea, so retailers can easily get their products, amenities, promotions and services in front of the AI, in a format that is easy for it to digest and cite properly.
I aim to solve this problem by giving retailers a single destination to update, a StreetTea Storefront. Since I posted my Storefront the page has been read by ten different companies, including OpenAI, Google, Apple, Amazon, Meta and Anthropic: 65 page reads, 129 photo fetches, 5 live AI lookups.
The numbers may not be very impressive yet, but the site and Storefront page aren’t even a full 2 months old yet. I’ll continue to update our progress. To increase your visibility, create a storefront, or claim your store if it’s already drafted at streettea.app.