{"id":73791,"date":"2025-08-11T11:43:42","date_gmt":"2025-08-11T06:13:42","guid":{"rendered":"https:\/\/www.tothenew.com\/blog\/?p=73791"},"modified":"2025-08-29T16:18:52","modified_gmt":"2025-08-29T10:48:52","slug":"empowering-a-food-delivery-app-with-ai-from-smart-tags-to-meal-planning","status":"publish","type":"post","link":"https:\/\/www.tothenew.com\/blog\/empowering-a-food-delivery-app-with-ai-from-smart-tags-to-meal-planning\/","title":{"rendered":"Empowering a Food Delivery App with AI: From Smart Tags to Meal Planning"},"content":{"rendered":"<p><strong>Introduction<\/strong><\/p>\n<p>The food delivery space is growing increasingly competitive, and personalisation is now more critical than ever. Our team recently had the opportunity to design and implement a suite of AI-driven features for a food delivery client\u2014each powered by OpenAI\u2019s latest models, intelligent agents, and Python-based apps. These features were crafted to elevate the user experience through smart automation and semantic understanding of food items.<\/p>\n<p>The goal wasn\u2019t just to enhance the user experience\u2014but to make ordering intuitive, insightful, and even fun. Below are the key innovations we brought to life:<\/p>\n<p><strong>1. Flavour Profiling: Quantifying Taste to Improve Recommendations<\/strong><br \/>\nWe developed a system to evaluate each dish based on its name, description, and image, quantifying it across five taste dimensions\u2014Savory, Spicy, Sweet, Earthy, and Fresh. Using OpenAI GPT-4-turbo, the system returns a taste profile in JSON format:<\/p>\n<p>{&#8220;Savory&#8221;: 85,&#8221;Spicy&#8221;: 10,&#8221;Sweet&#8221;: 5,&#8221;Earthy&#8221;: 30,&#8221;Fresh&#8221;: 70}<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p>&#8220;Shrimp Scampi with Linguine&#8221; \u2192 Savory: 80, Spicy: 15, Sweet: 5, Earthy: 5, Fresh: 10<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Powering flavour-based filters in the UI (e.g., \u201cShow me fresh &amp; spicy items\u201d)<\/li>\n<li>Driving personalised recommendations<\/li>\n<li>Visualising items with radar charts or flavour wheels<\/li>\n<li>Ensuring robustness through error handling, even with missing data<\/li>\n<\/ul>\n<p><strong>2. Smart Tagging: Categorising Food with Precision<\/strong><\/p>\n<p>We implemented a tagging system using OpenAI prompts and business rules to auto-generate three types of tags:<\/p>\n<ul>\n<li><strong>Visible Tags:<\/strong> Displayed to users (e.g., Poultry, Wraps)<\/li>\n<li><strong>Hidden Tags:<\/strong> Internal use for logic and analytics (e.g., Entree, Side)<\/li>\n<li><strong>Allergen\/Diet Tags:<\/strong> Added when explicitly mentioned (e.g., Contains Eggs, Vegan)<\/li>\n<\/ul>\n<p><strong>Example:<\/strong><\/p>\n<p>&#8220;Sweet Shoyu Tofu&#8221; \u2192 Vegan, Contains Soy, Contains Sesame<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Creating a clean, accurate taxonomy of menu items<\/li>\n<li>Preventing misleading assumptions<\/li>\n<li>Enhancing filtering, compliance, and personalisation<\/li>\n<\/ul>\n<p><strong>3. AI Review Generator: Turning Ratings into Words<\/strong><\/p>\n<p>We built a generator that creates short, natural-sounding reviews from ratings and feedback. It selects from over 15 \u201cvoices\u201d (e.g., The Minimalist, The Food Enthusiast) and adjusts tone based on the rating.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Rating: 5 stars (Voice: Minimalist)<\/strong><br \/>\n\u201cEverything was spot on. Loved the grilled chicken bowl \u2014 fresh, filling, and flavorful.\u201d<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Transforming bland ratings into engaging content<\/li>\n<li>Enhancing authenticity and boosting UX<\/li>\n<li>Automating feedback processing at scale<\/li>\n<\/ul>\n<p><strong>4. AI Meal Recommender<\/strong><\/p>\n<p>Users can type what they&#8217;re craving, and the system returns matching dishes via the meal recommender agent.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Input:<\/strong>\u00a0 \u201cI\u2019m craving something spicy.\u201d<br \/>\n<strong>Output:<\/strong><\/p>\n<ul>\n<li>Spicy Chicken Tikka Wrap \u2013 Bold flavours with a kick.<\/li>\n<li>Buffalo Cauliflower Bites \u2013 Crispy, zesty, and plant-based.<\/li>\n<\/ul>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Ideal for indecisive users or those in a hurry<\/li>\n<li>Integrates well with homepages, voice assistants, or chatbots<\/li>\n<li>Can plug into health or mood-based personalisation engines<\/li>\n<\/ul>\n<p><strong>5. Conversational Order Assistant<\/strong><\/p>\n<p>We enabled a natural interface where users can ask questions like, \u201cWhat did I eat last week?\u201d The assistant uses past order history and the current menu to return intelligent responses.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>User:<\/strong> \u201cWhat did I order last Friday?\u201d<br \/>\n<strong>Output:<\/strong> \u201cYou ordered the Grilled Chicken Wrap and Sweet Potato Fries.\u201d<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Human-like reordering experience<\/li>\n<li>Conversational UX for food delivery<\/li>\n<li>Aids retention by helping users rediscover favorites<\/li>\n<\/ul>\n<p><strong>6. Image-Based Food Search<\/strong><\/p>\n<p>Users upload a dish photo, and the image search agent identifies it using GPT-4o Vision, then recommends similar dishes.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Uploaded Image:<\/strong> Sushi Platter<br \/>\n<strong>Output:<\/strong><\/p>\n<p><strong>Identified:<\/strong> Sushi Platter<br \/>\n<strong>Suggestions:<\/strong> Tuna Nigiri, Sashimi Combo, Dragon Roll<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Enables discovery when dish names are unknown<\/li>\n<li>Powers camera-based ordering<\/li>\n<li>Enhances visual UX<\/li>\n<\/ul>\n<p><strong>7. Weekly Meal Planner<\/strong><\/p>\n<p>The meal planner agent generates a 7-day meal plan (breakfast, lunch, dinner) based on dietary preference and allergies.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Input:<\/strong> Diet \u2013 Vegan, Allergies \u2013 Dairy<br \/>\n<strong>Output (Monday):<\/strong><\/p>\n<p><strong>Breakfast:<\/strong> Oatmeal with almond butter<br \/>\n<strong>Lunch:<\/strong> Quinoa and chickpea salad<br \/>\n<strong>Dinner:<\/strong> Lentil curry with brown rice<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Simplifies health-focused meal planning<\/li>\n<li>Ideal for families and busy professionals<\/li>\n<li>Can drive subscription-based meal-kit offerings<\/li>\n<\/ul>\n<p><strong>8. Multilingual Ordering Assistant<\/strong><\/p>\n<p>Users input orders in English, and the assistant translates them into a selected language using Google Translate.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Input:<\/strong> \u201cOne large Margherita pizza, please.\u201d<br \/>\n<strong>Output (French):<\/strong> \u201cUne grande pizza Margherita, s&#8217;il vous pla\u00eet.\u201d<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Enhances accessibility in multilingual markets<\/li>\n<li>Simplifies localisation<\/li>\n<li>Improves customer support globally<\/li>\n<\/ul>\n<p><strong>9. Voice-Based Ordering<\/strong><\/p>\n<p>Simulates voice ordering using text as voice input. The assistant parses the request and updates the cart conversationally.<\/p>\n<p><strong>Example:<\/strong><\/p>\n<p><strong>Input:<\/strong> \u201cAdd two chicken tacos and a Coke to my cart.\u201d<br \/>\n<strong>Output:<\/strong> \u201cYou might be hungry! Adding chicken tacos and a Coke to your order.\u201d<\/p>\n<p><strong>Uses:<\/strong><\/p>\n<ul>\n<li>Powers hands-free interfaces<\/li>\n<li>Ideal for mobile-first or accessibility-centric apps<\/li>\n<li>Compatible with voice-to-text APIs<\/li>\n<\/ul>\n<p><strong>Behind the Scenes: How It Works<\/strong><\/p>\n<ul>\n<li><strong>OpenAI Agents:<\/strong> Each assistant is a custom GPT agent with carefully engineered prompts<\/li>\n<li><strong>Async APIs:<\/strong> Ensures fast, non-blocking interactions<\/li>\n<li><strong>Calorie API:<\/strong> Pulls nutritional data via CalorieNinjas or web scraping<\/li>\n<li><strong>PDF Generator:<\/strong> Meal plans exportable via fpdf<\/li>\n<\/ul>\n<p><strong>Final Thoughts:<\/strong> Smart, Scalable, Delightful<\/p>\n<p>These AI-powered features delivered measurable value:<\/p>\n<ul>\n<li>Faster decision-making<\/li>\n<li>Highly personalised ordering<\/li>\n<li>Multilingual and multimodal support<\/li>\n<li>Operational efficiency for the business<\/li>\n<li>By combining GPT agents, vision AI, and Python backends, we layered intelligence on top of a traditional food delivery app\u2014delivering real, scalable outcomes.<\/li>\n<\/ul>\n<p><strong>Want to Build Something Like This?<\/strong><\/p>\n<p>If your team is exploring AI in food tech, personalisation, or customer experience, we\u2019d love to connect. This architecture is flexible, future-proof, and ready to adapt to your platform.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Introduction The food delivery space is growing increasingly competitive, and personalisation is now more critical than ever. Our team recently had the opportunity to design and implement a suite of AI-driven features for a food delivery client\u2014each powered by OpenAI\u2019s latest models, intelligent agents, and Python-based apps. These features were crafted to elevate the user [&hellip;]<\/p>\n","protected":false},"author":2120,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"iawp_total_views":56},"categories":[5871],"tags":[7714,7715],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/posts\/73791"}],"collection":[{"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/users\/2120"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/comments?post=73791"}],"version-history":[{"count":5,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/posts\/73791\/revisions"}],"predecessor-version":[{"id":74054,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/posts\/73791\/revisions\/74054"}],"wp:attachment":[{"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/media?parent=73791"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/categories?post=73791"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tothenew.com\/blog\/wp-json\/wp\/v2\/tags?post=73791"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}