> Tastet

The Popularity of Our Platform has Led Tastet to Interactions with More than Two Million Users which Means that Our Product is Key to Their Gastronomic-Related Outings

Tastet was born out of the passion for local gastronomy and the desire to share stories about where to enjoy the best of Montreal’s food scene. Since 2016 the popularity of our platform has led Tastet to interactions with more than two million users which means that our product is key to their gastronomic-related outings. This active participation of our users and their consequent data flow allows Tastet to leverage their habits with the goal of raising their degree of satisfaction (35m per year).

We only include addresses that are of a proven quality, and despite such a large audience (2m), individual preferences nevertheless remain unique. The NextAI adventure is providing Tastet with the possibility to make the most out of our users’ interactions in order to optimize each of their experiences. By strategically collecting our users’ data, we better understand their habits and can subsequently transfer what they value – plus aspects they wish to avoid – into their choices for eating out.

The analysis of these behaviors, together with the addresses listed in our database, offer Tastet the ability to individualize recommendations, in line with the unique personality of the user. By optimizing this level of satisfaction for those users who are on a quest to discover local food, we also hope to nourish Quebec’s cultural fabric. And when taken in the context of this difficult year 2020, the cultural and gastronomic landscape has been greatly affected.

Innovation and AI forges social connections on a local level between restaurant owners, local producers and consumers. There are additional features on the Tastet platform that aim to leverage innovation, not only towards maximizing user experience but also to strengthen the social fabric that brings our local food industries together.

History of Tastet:

  • Passion for gastronomy
  • Acclaimed
  • Data collection
  • Individual preferences & differences

Innovation Objectives:

  • Hyper-personalized recommendations
  • Optimize user satisfaction
  • AI to make the right connections
  • Multiple languages

Future of Innovation:

  • To work alongside hyper-personalized recommendations
  • To participate in the integration of local food resources (consumers – restaurant owners – producers)
  • To use technology towards stimulating the local economy
  • To encourage lifestyle habits


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