apriloBot

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  First Screen

Inspiration

While major shopping platforms like Flipkart have advanced support systems, many independent vendors run their e-commerce stores using platforms like Magento, Laravel, and BigCommerce. These platforms often lack a unified, intelligent support system for handling customer queries, product issues, or order tracking. Customers today expect real-time assistance, accurate information, and seamless interaction, but these small-to-medium platforms often rely on basic contact forms or manual replies. This gap inspired us to create ApriloBot—an AI-powered virtual assistant that brings smart, instant support to any e-commerce platform.

What it does

ApriloBot is a smart chatbot that integrates with various e-commerce frameworks to: Provide instant answers about products, ratings, and availability. Let customers raise complaints or issues easily. Track orders in real-time using order IDs. Recommend popular or highly rated products. Escalate unresolved queries to human support when needed. It's customizable and can be deployed on any vendor's website to improve customer engagement and reduce support overhead.

How I Built it

Frontend Built using React, providing a clean and responsive chat interface. AI Layer Integrated with Ollama and LLaMA 3.1 for natural language Backend Developed with PHP Laravel, which manages authentication, user tickets, and API endpoints. E-commerce Integration: Custom Laravel APIs were developed to connect with platform databases (orders, products, etc.).

Challenges I ran into

Fine-tuning LLaMA 3.1 for e-commerce-specific intents and handling ambiguous customer queries. Creating a common data model to connect with different types of product databases (Magento, Laravel-based, etc.). Handling mixed-language inputs and building a fallback system for unclear queries. Making the bot lightweight for fast interaction while running locally using Ollama.

Accomplishments that I'm proud of

Built an AI bot that runs locally using Ollama, avoiding cloud dependency. Achieved high response accuracy using fine-tuned LLaMA 3.1 for product and order-based questions. Successfully integrated with live Laravel-based e-commerce platforms during demo. Created a reusable architecture that works with any e-commerce database with minimal setup.

What I learned

Real-world application of LLaMA models in customer support automation. Bridging AI with traditional e-commerce backends using PHP and Laravel. Handling conversational context using Ollama's lightweight local model deployment. Managing bot state, fallbacks, and multilingual handling in React.

What's next for apriloBot

Add voice interaction support for a more conversational UX. Train LLaMA further using customer support tickets for better contextual understanding. Create a plugin version for Magento, WooCommerce, and Laravel shops. Add analytics to show product interest, common issues, and customer satisfaction scores. Offer ApriloBot as a SaaS with easy deployment steps for non-technical store owners.

Built With

• llama3
• magento
• ollama
• php
• react