Jon Springer: Tastry is a
recommendation engine based on
having “taught a computer how to
taste.” Can you describe how it works?
Katerina Axelsson: Tastry is a SaaS
(software as a service) and insights
company, and a multidisciplinary innovator
in machine learning, analytical chemistry
and flavor chemistry. We created methods
to predict how sensory-based products like
wine, beverages, foods and fine fragrances
will be perceived by consumers in general,
and any specific individual in particular.
We like to say that we “taught a computer
how to taste” because our core innovations
are in our ability to break down the “flavor
matrix” and how it relates to products and
consumers. And we provide these insights
to manufacturers, distributors, retailers
What was the inspiration for
In early 2016, I was finishing my chemistry
degree at Cal Poly, which is located in the
central coast of California in wine country.
To pay my way through college, I worked at
a winery and custom crush facility.
During this time, I noticed many
idiosyncrasies within the wine industry. For
example, you could have a 10,000-gallon
tank of wine and sell half the blend to one
customer and half to another. And the
same wine could go under two different
bottles, two different labels, and then sell
for a different price and even receive a
different score from the same critics. I saw
an opportunity to pursue a more data-driven
approach to winemaking that could benefit
consumers, producers and ultimately the
entire supply chain.
In free time, I ran some experiments,
where I developed an approach and
methodology to measure the compounds
of sensory-based products like wine in a
way that could be more closely attributed
to consumer perception, and during the
course of analyzing hundreds of wines, I had
a series of breakthroughs and uncovered
interesting insights that would benefit the
field of flavor chemistry.
In your recent Groceryshop address, you
mentioned that Tastry helped to bring
category sales in wine up by 3% to 5%—
and margins by 18%. How?
It’s all about going beyond personalization.
We match the consumer preferences to
specific products with a high degree of
accuracy; we are 45% better at matching
consumers to products they will like than
the consumers themselves—or any other
method they would use to buy a product or
get a product recommendation. The odds
were that the shoppers were buying the
lower-margin wine that they’re typically
exposed to, and we organically steered them
to other brands that they wouldn’t have
discovered on their own. We also saw a 35%
increase in wine shopper satisfaction.
Aren’t some food purchases about
things other than taste? An eye-catching
package, a cool brand, a hot price? How,
if at all, can a recommendation engine
account for these subjective factors?
This is true for the first-time purchase.
However, the customer will not return to
the product if they didn’t like it. Tastry
helps brands and retailers drive repeat
visits and revenue, as well as customer
satisfaction. As soon as a shopper buys into
the Tastry recommender, they are more
likely to try new products.
If AI works for wine recommendations,
would it not also work elsewhere in the
Tastry uses proprietary AI to automate food
pairing to the wine based on the consumer’s
preferences. We pair the wines in the
store with over 1.5 million recipes that we
recommend. We are also launching our beer
recommender in late 2019.
Katerina Axelsson is the founder
and CEO of San Luis Obispo,
Calif.-based Tastry, an AI-powered
taste recommendation app used in
grocery store wine departments.
BREAKROOM A one-on-one conversation with an industry impresario
Coolest places to shop for food
around San Luis Obispo? The local
farmers market every Thursday night
What was your first job? I worked at
a yogurt and panini shop by the beach.
Burrito or burrito bowl? Burrito
bowl—more room for the good stuff. Read the full conversation at WinsightGroceryBusiness.com.