AI-Powered chatbot for Personalized Supplement Recommendations

An AI-powered chatbot that helps users discover the right health supplements through a quiz or simple conversation. It understands health goals or symptoms and recommends products.

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OVERVIEW

Project Overview

This project aimed to ease the supplement selection process for customers. We developed an AI chatbot that works with users through a guided quiz or natural conversation. Given the user inputs, recommendations are made by the chatbot based on health concerns such as digestion, energy, or immunity.

This system is directly linked to a PostgreSQL database, which means all recommendations are spot-on and current. Whether users describe their symptoms or opt for the quiz, the chatbot retrieves relevant products in real time—delivering a tailored, automated experience right on the website.

We also focused on designing the chatbot to be intuitive and easy to use. The design and flow of interactions were thoughtfully crafted to help users navigate easily, whether they’re just browsing or searching for specific health solutions.

Key Features

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01.

Smart Quiz-Based Interaction

To start each session, the chatbot runs a one-time interactive quiz to learn the user’s health focus. Their inputs are mapped to product attributes within the database so that the chatbot can quickly and efficiently provide users with relevant supplement recommendations immediately following quiz completion.

02.

Conversational Health Query Support

For users who skip the quiz, the chatbot changes to conversation mode. It listens for health-related inputs, for example, “I’m feeling low” and ask follow-up questions to gather the necessary context.

Client Retention Insights
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03.

Real-Time Product Recommendation Engine

All suggestions come from a PostgreSQL database with detailed product info like uses, ingredients, and benefits. The chatbot shows product cards with names, short descriptions, and buy links. It uses keyword-based logic to match user input, and an admin panel lets the admin user train the chatbot by uploading keywords and content.

Features at a Glance

  • trumanPersonalized health quiz

    trumanChat-based symptom analysis

    trumanFollow-up health questions

    trumanInstant product suggestions

    trumanReal-time product matching

    trumanProduct cards with links

    trumanAdmin panel for training and updates

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Project Challenges and Their Solutions

1. Continuous Refinement for Keyword Mapping and Chatbot Behavior

Challenges

Throughout the project, the client provided valuable feedback. The primary concern was improving chatbot responses and keyword accuracy. Although these refinements continued with the system, they were beneficial in getting the chatbot closer to user or brand expectations.

SOLUTIONS

To support this type of continuous improvement, we wanted to create a way for the system to be flexible and easy to maintain. In the admin panel, we set up a way for updates to be made (when necessary) from uploading one or two documents (PDF or Word files). This allowed the client to update keyword mappings with source documents or to update the chatbot logic without involving a developer. This ensured quick iteration cycles and fine-tuned user experiences, even post-deployment.

2. Real-Time Product Suggestions from Natural Language

Challenges

A primary requirement was the ability to offer product suggestions based on user input in real-time — structured quiz responses, or conversational health queries. The system needed to pull meaningful insights from user language and respond appropriately and reliably with relevant supplement suggestions, without relying on third-party APIs or pre-produced product filters.

SOLUTIONS

We implemented a robust data retrieval layer using PostgreSQL and File Search Integration tool. After processing user input, and their follow up responses, the system utilized key context from the user input and matched into the relevant real-time product data. This architecture allowed for a fast and reliable suggestion process and an overall seamless user experience facilitated by live database logic.

Technology Stack

Next.js

Used to build the interactive frontend interface, enabling smooth and fast user interactions with the AI chatbot.

React (with Vite)

Powers the admin interface, offering a lightweight, high-performance environment for managing chatbot data and analytics.

Python with FastAPI

Handles server-side logic and API communication, processing user inputs and managing real-time data flow.

PostgreSQL

Stores dynamic product information and health-related mappings used for generating personalized supplement recommendations.

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SEAN HYNES

Leader of the Effort

Sean is an AI/ML Engineer having over 20+ years of collective experience in the Tech industry. He leads end-to-end AI development processes, integrating cutting-edge technologies to deliver user-centric solutions. His expertise spans research, conceptualization, wireframing, interactive prototyping, and the design of intuitive user interfaces. 

He is capable of overseeing projects through all stages, from architecture building to crafting actual layouts and focuses on leveraging artificial intelligence for optimal outcomes.

Our team at Biz4Group is well experienced in manifesting innovative ideas for multi-dollar projects.

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