Case Studies·Enterprise Software

Enterprise RAG with LangGraph: 95% Faster Internal Knowledge Search Across Fragmented Data Sources

How an enterprise team replaced slow, manual cross-system searches with a LangGraph RAG intelligence hub — centralising OneDrive, YouTube, and internal documents into one semantic search layer with cited, source-grounded answers.

Enterprise RAG with LangGraph: 95% Faster Internal Knowledge Search Across Fragmented Data Sources

The Challenge

The client, a high-growth enterprise software firm, managed a massive repository of technical documentation, training videos, and corporate assets across disconnected silos including Microsoft OneDrive, YouTube, and localized server clusters.

Core Problem

Engineers and support staff were spending up to 6 hours per week manually searching for specific technical answers. This fragmentation resulted in a 12% increase in duplicate support tickets and significant 'knowledge leakage' as critical insights remained buried in hours of video content and nested cloud folders.
Without a centralized intelligence layer, the client faced mounting operational costs and a projected 20% slowdown in product deployment cycles due to the inability to rapidly onboard new developers and resolve complex technical queries.

The Solution

EnDevSols architected a high-performance Retrieval-Augmented Generation (RAG) backend utilizing FastAPI for asynchronous processing and LangGraph for complex, stateful agentic workflows. We built a custom ingestion engine that autonomously syncs and indexes data from OneDrive and YouTube into a Qdrant vector database, transforming passive assets into a searchable, semantic knowledge graph.

Tech Stack

FastAPILangGraphQdrantPythonOpenAI GPT-4Microsoft Graph APIJWT AuthenticationDocker

Key Results

Measured Impact

  • 95% reduction in information retrieval time for technical teams
  • 100% automated synchronization across YouTube, OneDrive, and local repositories
  • 22% reduction in operational overhead via granular token monitoring and optimization

Values & Impact

  • Significant boost in developer morale by eliminating repetitive search tasks
  • Enhanced data security posture through robust JWT-based authentication and audit logs
  • Improved accuracy in support ticket resolution

"EnDevSols didn't just build a chatbot; they built an enterprise nervous system. Our teams now have instant, unified access to the collective brain of the company. The ROI in terms of time saved was visible within the first month."

CTO, Global Enterprise SaaS Provider

Core AI Services Used in Projects Like This

RAG Systems & AI AssistantsDocument Q&A, knowledge bases, enterprise search
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Enterprise RAG with LangGraph: 95% Faster Internal Knowledge Search Across Fragmented Data Sources | Case Study | EnDevSols