SUCCESS STORY
Case Study: AI Agent for Indexing and Querying Content

AI Agent for Indexing and Querying Content

ThirdEye Data designed and implemented an intelligent AI agent to empower organizations with highly efficient, context-aware search capabilities across large volumes of content. By combining Azure AI Search with OpenAI-generated embeddings, the solution delivers advanced natural language understanding, full-text search, and semantic filtering. The application is built with a fully serverless architecture and globally distributed front-end, ensuring performance, scalability, and minimal infrastructure management.

THE CUSTOMER

The customer is a technology-focused organization managing growing volumes of internal and external content assets, including research articles, documentation, knowledge bases, FAQs, and policy documents. Traditional keyword search methods proved inefficient in surfacing the right information at the right time, and the teams needed a smarter, more intuitive way to find relevant content across their repositories.

BUSINESS GOALS OR CHALLENGES

Business Goals or Challenges

  • Smarter Search Capabilities: Provide employees with accurate and context-aware results for natural language queries.
  • Unified Search Experience: Enable semantic search across diverse content sources in a consistent and scalable manner.
  • Cloud-Native Deployment: Use a modern, serverless, and globally accessible architecture to reduce management overhead.
  • High Performance at Scale: Ensure quick and relevant responses even as the content base grows continuously

Understanding the Challenges:

  • Inadequate Traditional Search: Basic keyword matching lacked the ability to understand query intent or context.
  • Unstructured and Scattered Data: Content existed in various forms and locations, making indexing and querying complex.
  • Latency and Scalability Issues: Legacy systems couldn’t deliver consistent performance as content and user load increased.
  • Infrastructure Overhead: Managing backend servers, scalability, and deployment pipelines was costly and time-consuming.
  • Limited AI Capabilities: Prior systems did not leverage modern NLP or semantic search technologies to enhance user experience.

Prerequisites and Preconditions:

Building a robust AI-powered indexing and querying solution required a combination of foundational elements:

  • Access to Data Sources: All documents and repositories to be indexed were made accessible through secure APIs or cloud storage.
  • Azure Environment: The customer’s infrastructure was hosted on Microsoft Azure, enabling seamless integration with services like Azure AI Search, Azure Functions, OpenAI APIs, and Static Web Apps.
  • Embedding and NLP Support: Enabled integration with OpenAI services to generate vector embeddings that capture semantic meaning.
  • Security and Access Control: All content was protected using enterprise-grade authentication and role-based access to ensure secure operations.
  • Real-Time Indexing: Provisioning for near real-time ingestion of new content to ensure up-to-date search results.

THE SOLUTION

ThirdEye Data developed an AI-powered search agent leveraging a modern, serverless architecture and cutting-edge AI tools. The solution enables intelligent, natural language querying across all indexed content with high responsiveness and contextual accuracy.

Solution Highlights:

  • Azure AI Search Integration: Utilized Azure AI Search to index and query vast datasets with full-text search, scoring, and filtering capabilities.

  • Semantic Search with OpenAI Embeddings: Incorporated OpenAI to generate vector embeddings of documents and queries, enabling semantic similarity matching.

  • Natural Language Understanding: Empowered users to search using plain language rather than strict keyword queries.

  • Serverless Backend with Azure Functions: Used Azure Functions to create scalable APIs that handle indexing and querying tasks without infrastructure overhead.

  • Static Web App Frontend: Hosted the interface using Azure Static Web Apps for low-latency, globally distributed access.

  • Real-Time Updates: Enabled near-instant content refresh with automated indexing of newly added documents.

VALUE CREATED

The AI agent developed by ThirdEye Data delivered tangible improvements in information retrieval, knowledge discovery, and internal productivity across the organization:

  • Improved Content Discoverability: Semantic search led to higher relevance in search results, improving content findability across departments.
  • Enhanced User Experience: Employees could now ask complex, natural-language questions and receive accurate answers without navigating through multiple tools.
  • Reduced Search Time: Smart indexing and full-text search capabilities significantly reduced the average time spent on finding relevant documents.
  • Scalable and Cost-Efficient: Serverless architecture ensured the solution scaled automatically with user demand while keeping operational costs low.
  • Increased Platform Usage: Higher relevance and speed of results led to a 38% increase in adoption and repeated usage of the internal search tool.
  • Operational Efficiency: The elimination of manual search and document navigation resulted in a 21% productivity boost in knowledge-intensive roles.
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