Custom AI Chatbots Built for Any Website
This platform enables businesses to integrate a custom chatbot trained on their own Website content, delivering fast, accurate, and context-aware responses through a Retrieval-Augmented Generation (RAG) pipeline.
Category:
AI & ML
My Role:
AIML Engineer
Businesses often want an AI chatbot that understands their own products, services, and documentation, rather than providing generic AI responses. Building a custom chatbot from scratch for every client is time-consuming and difficult to maintain.
This project provides a reusable AI chatbot platform that can be integrated into any website and customized with the client's own knowledge base. Instead of developing a new chatbot for each business, the same platform can power multiple independent chatbots, each delivering accurate, business-specific answers.
Approach in this project
I started by collecting business-specific information from sources such as websites, FAQs, documents, and other knowledge repositories. This content is then cleaned, organized, categorized, and divided into smaller, meaningful chunks to improve retrieval accuracy.
Each chunk is converted into vector embeddings using a sentence transformer model, enabling semantic understanding rather than simple keyword matching.
These embeddings are stored in ChromaDB, where every client has a dedicated collection to ensure secure data isolation and support a multi-tenant architecture.
When a website user submits a query, the question is transformed into an embedding and compared against the relevant client's knowledge base using semantic similarity search. The system retrieves the most relevant content and passes it, along with the user's query, to a Large Language Model (LLM) through a Retrieval-Augmented Generation (RAG) pipeline. By grounding the LLM with business-specific context, the chatbot generates accurate, relevant, and context-aware responses while minimizing hallucinations.
The final response is then delivered instantly through a responsive chatbot widget that can be seamlessly embedded into any website, providing businesses with a customized AI assistant tailored to their own content.
The project resulted in a scalable, reusable AI chatbot platform that enables businesses to deploy their own intelligent website assistant. By combining Retrieval-Augmented Generation (RAG) with semantic search, the chatbot generates responses grounded in each client's knowledge base, improving accuracy and reducing AI hallucinations. The multi-tenant architecture allows multiple businesses to use the same platform while maintaining completely isolated knowledge bases, making the solution secure, efficient, and easy to scale. Its modular design also simplifies onboarding new clients, reducing deployment time and enabling customization for businesses across different industries.
Completed my first end-to-end AI and RAG-based application, gaining hands-on experience across the complete development lifecycle—from knowledge base creation and vector search to LLM integration and frontend deployment.
Conceptualized and designed the entire platform from scratch as an original idea, creating a reusable AI chatbot solution that can be customized and deployed across different business websites.
Followed a modular and scalable architecture, making it easy to onboard new clients and extend the platform with additional AI capabilities.
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