1. Overview
At Gravity Base, we built an AI-powered website chatbot hosted entirely on AWS. The goal was to create an intelligent, scalable, and efficient chatbot capable of answering user queries directly from website content. The system was designed by our AI team using AWS Bedrock, Gemini LLM, and other AWS open-source services to handle vector storage, website crawling, and front-end hosting.
2. Problem Statement
Large websites are difficult to navigate, and users often fail to find specific information quickly. Traditional search and manual chatbots don’t scale as content changes. The challenge was to deliver accurate answers, automatically updated, using a secure AWS-based architecture.
- Understands natural language queries in English,
- Retrieves accurate answers from website content,
- Updates automatically as the website changes,
- Operates efficiently within AWS infrastructure.
Without understanding these elements, digital marketers could not effectively tailor their content to appear within ChatGPT-generated results, limiting SEO effectiveness and visibility in conversational AI responses.
3. Proposed Solution
To address this challenge, we built an AI-based website chatbot using AWS cloud components and modern NLP techniques. The solution was based on a Retrieval-Augmented Generation (RAG) approach, integrating website crawling, vector embeddings, and a Large Language Model (LLM).
- AWS Bedrock: Used as the core AI service to host and manage the Gemini model for natural language understanding and response generation.
- Gemini Model (LLM): Handled text comprehension and response synthesis in English.
- Vector Database (AWS Open-Search Service): Stores website content embeddings for fast semantic retrieval.
- Website Crawler (AWS Service): Regularly crawled the website at scheduled intervals, extracted updated data, and generated new embeddings to keep the database current.
4. System Architecture
The system follows a six-step pipeline: website crawling, embedding generation, vector storage, semantic retrieval, LLM response generation, and browser-based delivery.
- Website Crawling
The crawler periodically scanned the website and sent the extracted textual data for embedding generation. - Embedding Storage
These embeddings were stored in the vector database for efficient similarity search. - Query Processing
When a user entered a query through the chatbot interface, the query was processed using Natural Language Processing (NLP) to generate query embeddings. - Semantic Retrieval
The system searched the vector database for the most relevant content. - LLM Generation Retrieved
context was then passed to the Gemini LLM via AWS Bedrock for context-aware response generation. - Response Delivery
The final, natural-language response was displayed to the user in the chatbot front-end.
5. Implementation Highlights
- Architecture Type: Retrieval-Augmented Generation (RAG)
- Language: English
- Cloud Platform: AWS (Bedrock, S3, and supporting services)
- Data Flow: Website data moves through the crawler, embeddings, vector DB, and query matching. Gemini then processes it and delivers the response.
- Hosting: AWS S3 for front-end deployment
6. Results and Benefits
Users could retrieve website information instantly using natural-language queries. The chatbot stayed current without manual updates, scaled with traffic, and reduced dependency on traditional navigation and search.
- Automated Information Access: Users could retrieve website information instantly using simple English queries.
- Dynamic Updates: The crawler ensured that the chatbot’s knowledge remained up-to-date with changes on the website.
- Scalability: We leveraged AWS’s serverless infrastructure to ensure scalable performance.
- Enhanced User Experience: Our solution delivered fast, context-aware, and accurate responses without requiring manual content updates.
7. Conclusion & Future Scope
The project demonstrated how AWS Bedrock, combined with Gemini LLM and open-source vector databases, can be used to build an intelligent, continuously learning chatbot. By implementing a RAG-based pipeline, the system achieved a balance between automation, accuracy, and scalability. This architecture can be extended to other domains, such as documentation assistants, knowledge base bots, or enterprise-level AI support systems.
At Gravity Base, a leading AI company, we apply this architecture to build scalable, production-ready AI systems across multiple business domains.