Marry internal enterprise data with external data
Platform > Patented AI

Marry internal enterprise data with external data

Our patented AI supports real-time personalization and recommendation using a blend of internal and external data

The difference

Map customer behavior according to their tastes, affinities, and preferences.
Simplify their choices with relevant recommendations


A living, breathing map of the world’s tastes acquires essential data on various lifestyle categories, to create a graph-based entity-affinity model. This model is then mapped to customer behavior data, to reveal a universe of choices and recommendations.

The result: 70% more effective than the top end offering in the market. At a fraction of the cost


The TasteGraph™ has over 6.5Mn merchants analyzed and mapped, including




other merchants

That’s not all comes with multiple features that make it a one-of-a-kind revenue acceleration engine



With over 6.5Mn merchants and counting, every digitally discoverable merchant is a target entity for the TasteGraph™. It identifies unique merchants globally and establishes relationships with other relevant merchants

Taste Attributes

Taste Attributes understands how customers make decisions in each lifestyle category. It identifies the attributes that define tastes in these categories. With cutting-edge machine learning and natural language processing (NLP), filters through a combination of structured metadata and unstructured data. It then tags every merchant with hundreds of such taste attributes and assigns scores to each, to create a taste profile of the merchant



Based on a merchant’s profile, provides an affinity score between any two merchants in each category. uses anonymized customer preferences and a patented collaborative method to create a cross-category graph. All in real-time



Complement your customers’ tastes with the right merchants. The TasteMatch provides an affinity score between a customer’s taste profile and a given merchant. The TasteMatch score is used to rank recommendations for customers. It considers context for specific use-cases

Choice AI

The math behind simple choice

Choice AI

Consumer choice relies on four components: taste, influence, context and behavior


Aggregated preferences of customers, like ratings and reviews


Other factors such as devices, time, location, weather and more uses these components to understand choice. We call it the Choice Equation 


External social interactions that affect a customer’s decisions, such as ‘likes’ and ‘shares’ 


Online and offline customer activity such as past transactions and interactions