Most of us spend quite a bit of time shopping. Depending on the circumstances, it can be either a chore, a pastime, or some combination of the two.
Online retailers get this, which is why they offer both addictively scrollable listings and very fast checkout. You can get what you want quickly, or, if you prefer, spend time browsing.
With the rise of generative AI, both types of shoppers look poised to see new tools that make the experience more enjoyable or efficient. In the startup sphere, this includes apps that understand detailed shopping requests, bots to scour secondhand offerings, and tools that simplify building custom products.
Using Crunchbase data, we identified more than a dozen companies funded in recent quarters with offerings tied to both AI and shopping. Collectively, companies on our list below have raised more than $280 million.
While there's no predominant business model among the companies that have raised funding, a common theme is around building tools that make it easier for a computer to understand exactly what a human wants.
Yes, it's true current offerings do this pretty well when you have a specific item in mind. Enter in a branded toy or book title, and odds are you'll quickly get the appropriate listing.
But in areas like fashion or furniture, where shoppers are seeking a particular look or style rather than a branded product, it's easy to get mired in search results. Whether it's "comfortable blue armchair" or "Halloween socks," a basic query tends to result in a near-endless scroll of results.
AI-enabled startups are attempting to improve this process through models that do a better job of matching descriptions with desired results.
Among the most heavily funded in this vein is San Francisco-based Daydream, an AI-enabled personalized shopping tool that raised $50 million this summer in a seed round led by Forerunner and Index Ventures.
While still a ways from its public launch, Daydream's website teases its planned offering with sample fashion searches that its tool will match with products. Examples include "bright colored sneakers that feel old school and classic" or "a dress for a summer wedding in Costa Rica."
Others are working with brands to apply more sophisticated language matching for customer queries. This includes Lily AI, a Mountain View, California-based startup that has raised over $70 million for an AI-powered tool to connect a brand's shoppers with items they'll want to buy.
Another example is Vantage Discovery, which closed a $16 million Series A in June. The San Francisco startup offers retailers and brands tools that allow their offerings to be discovered via natural language semantic searches as well as keyword queries.
Startups are also employing AI to customize and offer concierge-type features to help shoppers find stuff they want to buy.
On the personalization front, Arcade, which picked up $12 million in January in rounds led by Ashton Kutcher and Reid Hoffman, operates a platform which uses AI models to design custom jewelry. Customers enter a description of their desired product, and an artist gets paid to make it.
FindMine, which landed an $8.9 million Series A this spring, offers an "AI stylist" that puts together outfits from a brand's collection for prospective customers. That, in turn, enables merchants to ring up higher-value purchases.
And Rep AI raised $8.2 million in August for an "AI concierge" that merchants can provide at scale to prospective customers. It targets sellers with large catalogs of offerings, who benefit from tools to help shoppers narrow down the array of choices to find the one that fits.
Berlin-based Faircado, meanwhile, is deploying AI for the secondhand market. Its app enables shoppers to see recommendations for comparable used items while looking to make a purchase.
It's not lost on investors that the rise of AI-powered commerce tools comes amid a period of unprecedented retail choices.
Kirsten Green, founder and managing partner of retail-focused venture firm Forerunner believes what we're currently seeing is a shift in consumer values intersecting with the rise of AI.
In a blog post on this theme, she postulated that "the values shift underway is born out of consumers' broad sense of fatigue and overwhelm from a culture that's now dominated by hyper-access and hyper-consumerism."
Our exhaustion with the endless supply of choices, she wrote, is giving rise to "a refreshed desire for experts and services that edit the vastness of what's available" and present the best possible options.
As one of those consumers often overwhelmed by the sheer number of things available to buy, I have to admit this sounds like a reasonable hypothesis. When shopping becomes work, why not outsource it to an AI bot?
At the same time, however, it's also clear that current retail offerings already provide myriad ways to deal with or avoid the overabundance of choices. This includes smaller-footprint grocery stores, highly honed recommendation engines from Amazon and big-box retailers, and shops that offer a targeted selection based on factors such as style and budget.
Could AI help make it even easier to part with our cash? Sure. But it's also worth recognizing that there are plenty of existing tools that are already doing a very good job of that.
