Hungryroot : AI-Driven Meal Planning & Grocery Customization Platform

Food Tech

Hungryroot – AI Meal Planning & Grocery Personalization Engine

Hungryroot teamed up with us to create a dynamic AI system that personalizes grocery orders and meal planning based on individual user goals. The platform analyzes dietary preferences, past purchases, allergies, and even time-of-day behavior to suggest smart grocery combinations. Our recommendation engine also adjusts portion sizes and recipes in real time based on household size or health targets. The result is an intuitive grocery experience that feels like it knows you. Customers not only enjoy personalized meals but also see reduced food waste. It’s AI that makes healthy eating automatic and enjoyable.

Project Overview

Client: Hungryroot (U.S.-based personalized grocery delivery and meal kit service)
Challenge: Customers struggled to find meals that aligned with their nutrition goals and shopping habits
Goal: Build a smart AI engine to:
- Recommend groceries and recipes personalized to diet, household, and lifestyle
- Adjust portion sizes and combinations dynamically based on user inputs
- Reduce food waste and maximize order relevance
Team: 9 (3 Data Scientists, 2 ML Engineers, 2 Full Stack Devs, 1 Nutrition Specialist, 1 PM)
Timeline: 5 months (AI Recommendation Core → Real-Time Adjustment Engine → Cross-Device Rollout)

“The AgixTech platform didn’t just personalize grocery lists—it reinvented how our users eat, shop, and feel about healthy food. This is food tech that feels human.”

Chief Product Officer, Hungryroot

The Challenge

Critical Pain Points:

Fixed recipes and standard order sizes led to food waste and underutilized ingredients
One-size-fits-all meal planning didn’t reflect lifestyle differences across households
Customers found it difficult to align orders with diet goals (e.g., macros, allergies, clean eating)

Technical Hurdles:

Designing AI logic that balanced taste, nutrition, cost, and convenience
Integrating dynamic grocery and recipe substitution based on inventory changes
Learning preferences at the household level, not just individual users

Tech Stack

Component	Technologies
Recommendation Engine	LightFM, XGBoost, TensorFlow, NLP-based Label Filters
Backend & APIs	Python, FastAPI, PostgreSQL, Redis
Grocery & Nutrition Data Sync	REST APIs, Firebase, USDA FoodData Central API
Mobile & Web Frontend	React Native, Tailwind, GraphQL
Data Tracking & Feedback Loop	Segment, Mixpanel, BigQuery, DBT
Personalization & Messaging	Braze, Iterable, Push API Integrations

Key Innovations

Grocery and meal suggestions adjusted for diet, preferences, and time-of-day habits. AI recalculated portions by household size and nutrition goals. Food waste dropped as carts became smarter and more relevant.

Hyper-Personalized Grocery Planning

Combines dietary preferences, health goals, and time-of-day patterns

Result: 47% increase in user retention after just 2 orders

Real-Time Smart Swaps

Suggests seamless ingredient replacements without disrupting the meal plan

Result: 35% reduction in customer service tickets about product unavailability

Household-Aware Recipe Scaling

Adapts portions and instructions to household size and food goals

Result: 29% reduction in weekly grocery waste across active users

Our AI/ML Architecture

Core Models

Meal & Grocery Recommendation Engine:
- Collaborative filtering + nutrition-aware ranking model
- Recommends combinations optimized for variety, balance, and diet
Portion & Household Adjustment Module:
- Predicts portion size needs based on age groups, past meal sizes, and plate waste feedback
- Dynamically recalculates grocery quantities and cooking instructions
Smart Swap & Suggestion Engine:
- Replaces unavailable or disliked items with compatible, diet-compliant alternatives
- Uses culinary graph embeddings and pantry reuse prediction

Data Pipeline

Sources
- User dietary preferences, allergy settings, previous orders, time-of-day cooking behavior
- Grocery catalog (inventory, nutrition info, pairings), household profiles