Research Librarian

 
 

Overview

The Research Librarian project was a groundbreaking initiative to develop an AI-powered index for UX and CI research. By leveraging advanced AI techniques similar to the Calvinist Parrot project, the aim was to enhance internal accessibility and search capabilities. This tool was envisioned as a dynamic solution to deepen users' understanding and interactions with the company's research data.

 

Goal

  • Create an AI-driven indexing system that provides rapid, accurate, and insightful access to UX and CI research data.

  • Continuously refine the AI's comprehension and search capabilities to allow deeper exploration of research data for internal users.

 

Solutions

The Research Librarian's primary features and enhancements include:

  • Library Indexing and Categorization: One of the pivotal challenges was indexing the extensive UX and CI research data. This challenge was addressed by developing an innovative algorithm (patent in process) designed to analyze research, establish multiple categories, and set up numerous query engines. This significant step transformed how the vast amount of research data became accessible and searchable.

  • Autonomous Query Execution: The system autonomously determines the most appropriate query engine to answer user searches, significantly improving the indexing system's efficiency in providing precise and insightful access points.

 

Findings

Implementing the Research Librarian led to substantial improvements:

  • Enhanced Data Retrieval: Integrating multiple query engines markedly improved data retrieval's speed and accuracy.

  • Improved Depth of Search: Tapping into the comprehensive UX and CI research data through specialized query engines enabled the system to deliver more profound and more informed results to users' inquiries.

  • Optimized User Experience: The system's autonomous nature, fortified by the state-of-the-art indexing algorithm, led to an enriched user experience, offering quicker and more pinpointed answers to a vast range of queries.

 

Takeaways

The Research Librarian project underscored several technical and innovative landmarks:

  • Algorithm Development: The conception of a unique algorithm for indexing and categorizing a vast reservoir of research data was a cornerstone achievement. This algorithm was fundamental in structuring the expansive UX and CI research into accessible and searchable categories.

  • Autonomous Query Execution: Crafting an autonomous system capable of pinpointing the right query engine marked a significant technological leap, ensuring more accurate and contextually relevant search results.

  • Multi-Container and Multi-Build Architecture for Performance Optimization: Embracing a robust multi-container and multi-build strategy was paramount. Each Docker container was designated a distinct query engine, facilitating the efficient management of the extensive research data. This architectural approach led to faster search durations, minimized resource consumption, and a streamlined user experience.