Transforming a 20-Year Archive into an Intelligent AI Assistant

Our client, a leading expert in their field, had amassed two decades of valuable, proprietary articles. This rich knowledge base was difficult for their audience to search and engage with in a meaningful way. The client had a vision to create an AI-powered system that could answer user questions based on their unique content, but as a non-technical founder, they didn't know anything about AI or the engineering required to build such a solution.

The primary goal was to create a working, intelligent prototype that could serve as the foundation for a new product offering. The key objectives were:

• Define and score key use cases for an AI assistant based on the existing content.

• Develop a robust methodology for ingesting, processing, and embedding the 20-year archive of articles.

• Build a full-stack RAG prototype that could accurately answer user queries using only the provided documents as a source.

• Create comprehensive documentation on how to implement the solution within an AWS infrastructure, process new documents, and evaluate the quality of user query responses.

Our engagement began with a deep-dive strategy session to define and prioritize potential use cases for the AI assistant. We then developed a custom data processing pipeline to handle the client's unique archive of documents.

The core of our technical solution was a custom-built RAG system. This involved setting up a vector database to store the embedded content and engineering a sophisticated retrieval and generation pipeline that used a Large Language Model (LLM) to provide accurate, context-aware answers based solely on the client's articles.

To demonstrate the system's capabilities, we built a full-stack prototype application that allowed for real-time interaction with the AI assistant.

Crucially, we delivered extensive documentation covering every aspect of the project. This included a guide on how to deploy and manage the application on AWS, a step-by-step process for adding new documents to the knowledge base, and a detailed methodology for scoring and evaluating the quality of the AI's responses to user queries, empowering the client with full control over their new platform.