Cocktail Recipes Bot
- Food & Beverage
- AI Chatbot
- LangChain
- PostgreSQL
- Web
- Mobile
- Cloud Development
- FoodTech
- Hospitality
- AI Development
- React
Overview of Our Client
Our client was an entertainment and hospitality-focused business aiming to refresh the cocktail experience for showbusiness venues, bars, and event organizers. Their goal was to provide visitors of the beverage web-platform with an intuitive tool to explore and select cocktails from a large recipe database, as well as deliver personalized recommendations and incorporate branded product placement.
The client required a system capable of processing user input, large-scale recipe search, and dynamic content delivery on web and mobile platforms, while integrating structured data for analytics and marketing purposes.
Challenge
In creating a cocktail recommendation platform, the main challenges included:
- Absence of a centralized instrument for selecting cocktail recipes adapted to event settings.
- Gathering and processing of user preferences through a dynamic questionnaire.
- Searching and ranking thousands of recipes using vector-based similarity search (RAG).
- Integrating branded product placement within the recipe selection experience without disrupting user interaction.
- Providing responsiveness on both web and mobile platforms for concurrent users.
Main Goals
To deliver the desired software solution, we decided to split the project into more manageable goals:
- Provide users with an AI-powered interactive assistant to explore and select cocktails based on their preferences.
- Enable dynamic recipe recommendation logic using similarity search and personalized vectors.
- Integrate product placement into the recommendation workflow for marketing purposes.
- Deliver a cross-platform experience tuned for web and mobile, ensuring fast and intuitive interactions.
Project Overview
We developed a cloud-native conversational platform combining AI, vector search, and structured recipe management. To provide scalability and performance, we used LangChain/LangGraph for the conversational engine and PostgreSQL for storing structured recipe and product data.
The platform executed live user questionnaires, mapped inputs into vector embeddings, and retrieved matching recipes. Branded ingredients and products were highlighted within the recommendations. The architecture supported horizontal scaling to manage multiple concurrent users without performance degradation.
Solution
The final solution served as both an intelligent recipe assistant and a marketing platform. The bot guided users through predefined questions to capture taste preferences & details about the upcoming party/event, filtered recipes using vector similarity search, and gave personalized recommendations with embedded product placement.
The system utilized a RAG (Retrieval-Augmented Generation) architecture to retrieve accurate recipe data from a local database using vector embeddings. Besides, the platform also provided analytics on user selections, allowing the client to optimize recipes, marketing campaigns, and product positioning. Both the web version (React) and the mobile app integrated with the AI assistant, providing a consistent cross-platform experience and improving overall engagement.
Key Features
- AI-powered conversational interface for cocktail selection.
- Questionnaire-based preference gathering and vector search for accurate recommendations.
- Product placement integrated directly into recipe suggestions.
- Dynamic recipe filtering and ranking based on user inputs.
- Scalable cross-platform web and mobile access.
- Analytics and reporting for recipe popularity and product engagement.
Technology Stack
To ensure fast response, accurate recipe matching, and storage of both structured and semantic data, we selected a lightweight yet ready-to-use technology stack that combines LLM orchestration tools with a strong relational database.
AI & Conversational Engine
- LangChain / LangGraph, LLM (ChatGPT)
Database
- PostgreSQL+pgvector for structured recipes and product data
Web & Mobile Platforms
- React, responsive mobile frameworks
Infrastructure
- Cloud-hosted for horizontal scaling and high concurrency
Analytics
- Embedded tracking for user interaction and product engagement
Core Team
- Backend Developers: Developed LangChain/LangGraph conversational flows and vector search integration. Implemented PostgreSQL data structures, query optimization, and recipe management logic.
- Frontend Developers: Built responsive web and mobile interfaces for user interaction.
- Data & Analytics Engineers: Designed engagement tracking and product placement reporting.
- DevOps & Cloud Architects: Managed deployment, load balancing, and uptime assurance.
Results
The platform provided an AI-powered, interactive cocktail selection experience. In particular, it achieved:
- Efficient user-guided selection of cocktail recipes through detailed questionnaires, reducing time-to-recipe selection by up to 60–70%.
- Fast and accurate recommendations using vector-based recipe search, improving recommendation relevance by ~30–40% compared to keyword-based filtering.
- Increased user engagement through AI-powered personalization and enhanced brand visibility via integrated product placement.
- Scalable performance on web and mobile platforms for high concurrency scenarios, supporting thousands of simultaneous users without degradation.