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TasteFit

TasteFit is a mobile app that matches diners with dishes based on their personal taste profile, current cravings, dietary needs, and dining occasion, rather than relying on average ratings. The app uses a TasteScore system that combines confidence and evidence dots to show how strongly a dish may fit, and it builds a TasteGraph that connects people, dishes, preferences, and real dining outcomes to improve recommendations over time.

Traction –/10
Bengaluru, India · HQ
  • Food Technology
  • Software Only
Updated 13 days ago

Funding

Funding rounds are not available yet.

Founders

Founder details are not available yet.

Product

Problem

Highly rated dishes on traditional platforms reflect the average of all reviewers, not whether a specific meal fits an individual's taste, craving, dietary constraints, or occasion. A great dish can still be the wrong dish for a particular person, leaving diners to guess through generic reviews and social posts.

Solution

TasteFit provides a mobile app that matches users with dishes based on their personal taste profile, current cravings, dietary needs, and desired dining occasion. The app displays a TasteScore for each dish, combining a confidence level with evidence dots that indicate how much supporting data exists behind the match. Users can save dishes to a personal library, marking them as Fire, Like, Not for Me, or Want to Try, creating a record of what worked and what did not. Each user's dining experience contributes to the TasteGraph, a taste intelligence system that connects people, dishes, preferences, and real dining outcomes. This shared data makes discovery better for everyone as the system learns which dishes fit which people in which moments.

Target Audience

Primary users are individual diners who want personalized meal recommendations and a way to track dishes they have enjoyed, as well as food enthusiasts who want to share their taste experiences to guide others.

Features

  • TasteScore system that combines confidence (how strongly the dish may match) with evidence dots (amount of relevant supporting dining outcomes)
  • Personal dish library that tracks ordered dishes by restaurant and date with Fire, Like, Not for Me, and Want to Try classifications
  • TasteGraph technology that connects people, dishes, preferences, trusted guidance, and real dining outcomes for personalized recommendations
  • Taste Champions program that recognizes users whose shared dining experiences consistently help others make better choices
  • Dish-level search and discovery with filters for proximity, dietary needs, budget, flavor preferences, and occasion
This profile is AI-generated and may contain inaccuracies.