Case Study
AI-Powered Conversational Chatbot for Next Gen Web Builders
Next Gen Web Builders is a digital solution organization in USA that needed a smart and secure chatbot to support the web visitors, clarify typical questions, and direct the users to the appropriate services and pages. What we came up with was a Retrieval-Augmented Generation (RAG) based conversational assistant consisting of open-source LLMs and semantic vector search that is deployed on-premise in its entirety as it offers full access and privacy.
Next Gen Web Builders engineers developed a context-sensitive AI chatbot in LLaMA 3, Milvus, Sentence Transformers, and Flask. The system also assists in codifying client engagement, reformulating exact answers to service-related questions, and creating steady, brand-corresponding communication, everything done inside the local and sheltered as well as scalable environment.
Industry
AI / Digital Solutions
Technologies
RAG, LLM, Milvus, Flask
Launched
2014
Country
UAE
About The Project
Branded Conversational AI
More than a simple chatbot, Next Gen Web Builders wanted a virtual assistant that is able to know the intents of the users and retrieve the business perspective, and respond in a way that sounds like the company itself. It is a lightweight but strong conversational system that can be integrated into their digital ecosystem, but its privacy is not infringed at all.
Overview
Our team created an open modular chatbot that receives user requests and reviews the pre-programmed responses, and, in case of necessity, searches documents in external Milvus before responding to them with the help of LLaMA 3. The assistant does all the tasks, including the FAQs, information about the projects, giving quick, precise answers, and redirecting users where it is required. The solution reduces the dependency on human support and increases engagement, and it also makes sure that responses are in line with brand tone and business sense.
Our Challenges
Problems that We Overcame by Using Next Gen Web Builders AI Chatbot
Restless Answers to Client Inquiries
Automation has avoided giving different answers to users. We implemented the use of an AI model that makes sure that all the responses are correct, nice, and are within the brand tone.
The Context-Ableness of Replies
Simple chatbots hardly come up with helpful responses. We incorporated the internally maintained structured files, such as FAQs, service metadata, and client portfolios, and used vector search to deliver context-aware responses.
LLM Leaks and Noise
To avoid misleading responses, we implemented fallback logic using LLaMA 3. When no context is found, it suggests relevant FAQs or brand-safe replies, ensuring accurate, on-brand responses even when user queries fall outside the dataset.
Constraints on Resources Associated
The system had to be locally fast in its servers. We have introduced optimization to the LLM and vector database so that it could be run in both CPU and GPU modes, and the performance would not be reduced.
Solutions
What We Delivered!
The new AI chatbot, now fully local at Next Gen Web Builders, allows customer input, distribution of quality service details, and the minimization of support overhead. The system, based on a secure and scalable architecture, is fast, brand-aligned, and available all the time.
Real-Time RAG-Based Chatbot
Semantic Retrieval with Milvus
Brand-Aligned LLM Responses via LLaMA 3
Flask-Powered Web Integration
