Case study :
Transforming Enterprise Knowledge Management with Generative AI and RAG
How a Global Client Improved Decision-Making with Context-Aware Document Retrieval and Chat
Client : A Global Enterprise (Confidential)
Industry: Cross-Industry Knowledge Management
Location: United States
A large enterprise sought to solve inefficiencies in accessing institutional knowledge spread across numerous unstructured documents.
“By leveraging RAG and Generative AI, the organization transformed its static document repositories into intelligent, conversational knowledge assistants—enabling faster information access, streamlined workflows, and more informed decision-making across teams.”
Vice President,
Corportae Event Management
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Overview
To address the challenge of fragmented enterprise knowledge, the client partnered with InfoObjects to implement a GenAI solution combining Retrieval-Augmented Generation (RAG) and LLMs. This AI-driven approach enabled semantic search, context-aware responses, and real-time interaction with large volumes of unstructured internal documentation.
Time Spent Searching for Information Reduced
Increase in Employee Productivity
User Query Accuracy
The Challenge
The enterprise faced several critical issues:
- Dispersed Knowledge Repositories: Information scattered across teams, formats, and legacy systems
- Inefficient Search: Traditional keyword search lacked context and relevance
- Employee Onboarding Delays: New employees struggled to locate key process and policy documents
- Scalability Concerns: Need for a solution that scales across departments without massive re-engineering
The client needed a system that could retrieve the right information, understand intent, and answer contextually in real-time.
InfoObjects Solution
Intelligent Document Parsing & Ingestion
InfoObjects built robust ingestion pipelines to process PDFs, Word docs, HTML files, and intranet content using:
- LangChain for document chunking
- FAISS vector databases for embedding storage
- Azure OpenAI and other LLMs for embedding generation and QA
RAG Architecture Deployment
We deployed a scalable Retrieval-Augmented Generation (RAG) framework:
- Semantic search over enterprise content
- GPT-powered natural language response engine
- Contextual answers pulling from relevant document segments
Personalized Knowledge Assistant
An enterprise chatbot powered by LangChain and GPT answered employee queries, cited documents, and improved with feedback.
Security & Access Control
Role-based access ensured users only accessed content they were authorized to view.
The Result
- Faster Decision-Making: Employees found relevant documents instantly with semantic search and citations
- Boosted Productivity: Reduction in repetitive queries and manual document searches
- Improved Onboarding: New hires ramped up quicker by querying the assistant for HR and operations info
Scalable Knowledge Retrieval: System scaled effortlessly across multiple business units
Conclusion
The deployment of RAG and Generative AI transformed the client’s knowledge management from static file storage to dynamic, intelligent interaction. The solution continues to evolve with new integrations and employee feedback, setting a benchmark for AI-led enterprise knowledge solutions.
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