AI & Machine Learning
1/11/2026

Intelligent Knowledge Discovery and Content Generation Platform

A large enterprise struggled with scattered information across platforms, hindering productivity and collaboration. By implementing an intelligent knowledge discovery platform, they unified organizational knowledge, drastically reducing search times and enhancing decision-making and collaboration.

AI Agents
Large Language Models (LLMs)
Neo4j
Next.js
PostgreSQL
Python
langgraph
milvus
Intelligent Knowledge Discovery and Content Generation Platform

Project Breakdown

Challenges / Assignment

A large enterprise faced challenges with scattered information across platforms like SharePoint and internal wikis.

  • Critical information was fragmented across multiple tools and platforms.
  • Employees spent excessive time searching for relevant information.
  • There was difficulty in finding context for meetings, project updates, and reports.
  • Collaboration was slow due to fragmented information access.
  • Productivity was reduced by repetitive information searches.
  • Knowledge silos prevented efficient information reuse.
  • There was inconsistent access to organizational knowledge across teams.

Solution

We developed an intelligent knowledge discovery platform to unify organizational knowledge.

  • Integrated major data repositories into a centralized knowledge infrastructure.
  • Implemented multimodal processing for documents, images, and structured data.
  • Deployed vector and graph databases for information ingestion and relationship mapping.
  • Built a document processing pipeline using messaging queue systems for scalability.
  • Used large language models for natural language understanding.
  • Developed a retrieval-augmented generation system for context-aware responses.
  • Created content generation capabilities for summaries and reports.

Result

The new platform significantly improved information access and productivity.

  • Dramatically reduced information search time from hours to seconds.
  • Provided a single unified interface, eliminating fragmented tool switching.
  • Delivered time-efficient, accurate answers from the entire knowledge base.
  • Automated content generation for meeting summaries, reports, and presentations.
  • Enhanced collaboration through faster information discovery.
  • Increased productivity by eliminating repetitive searches.
  • Promoted better knowledge reuse across departments and teams.
  • Enabled enhanced decision-making with quick access to relevant context.

Project Details

Category

AI & Machine Learning

Last Updated

15th Jan 2026

Duration

6 months

Team Size

2

Client Industry

IT Services

Views

10+ views

Technologies Used

AI Agents
Large Language Models (LLMs)
Neo4j
Next.js
PostgreSQL
Python
langgraph
milvus

About the Company

This case study was created by Inspiring Lab