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Teaching machines to think...

Recommendation Engine

Personalized dish recommendations based on preferences, allergies, and health goals

Food Tech
Recommendation Systems
Personalization
Mobile Integration
Food Recommendation Assistant

About the Project

We built a sophisticated recommendation engine that suggests dishes tailored to individual user preferences, dietary restrictions, allergies, and health goals. The system learns from user behavior and feedback to continuously improve its recommendations, driving higher customer satisfaction and increased revenue through personalized upselling.

Challenges & Solutions

Key obstacles we addressed and the innovative solutions we delivered.

Generic recommendations

Pain Point

One-size-fits-all suggestions led to low engagement and frequent customer complaints about irrelevant options

Our Solution

Developed multi-factor recommendation model considering taste preferences, dietary needs, and contextual signals

Allergy safety

Pain Point

Manual allergen tracking was error-prone, creating liability risks and limiting options for customers with restrictions

Our Solution

Built comprehensive ingredient database with automatic allergen flagging and safe alternative suggestions

Cold start problem

Pain Point

New users received poor recommendations until sufficient data was collected, causing early churn

Our Solution

Implemented smart onboarding flow and collaborative filtering to provide quality recommendations from day one

Results

Measurable outcomes that demonstrate the impact of our work.

20%

Higher Customer Satisfaction

15%

Revenue Increase

35%

Higher Engagement

<100ms

Response Time

Let's Build Your AI Solution

From first prototype to scaled production—one team handles it all. Tell us what problem you're solving.