An intelligent, role-based document management and AI-powered knowledge retrieval system built for organizations and their teams.
Project Overview
KnowledgeFlow is an enterprise-focused Retrieval-Augmented Generation (RAG) platform that enables companies to centralize their internal knowledge and make it accessible through AI-powered conversations.
Instead of manually searching through hundreds of documents, employees can upload company resources, ask questions in natural language, and receive contextual answers grounded in their organization's uploaded knowledge.
The platform supports multiple employees, centralized administration, and hierarchical access through three distinct roles: Super Admin, Admin, and Employee.
Core Features & Capabilities
1. AI-Powered Document Intelligence
Upload and manage organizational documents.
Automated document processing, text extraction, and chunking.
Generate vector embeddings for semantic retrieval.
Context-aware responses using relevant document content.
Conversational interaction with company knowledge.
Streaming AI responses for a responsive chat experience.
2. Enterprise RAG Architecture
Custom Retrieval-Augmented Generation pipeline.
Vector-based semantic search using MongoDB Atlas Vector Search.
Configurable chunking strategy with 1,000-character chunks and 50-character overlap.
Top-K retrieval to identify the most relevant document segments.
Context injection into LLM prompts for grounded responses.
Reduced dependency on generic AI knowledge by retrieving relevant organizational information.
3. Multi-User & Role-Based Access Control
The platform is structured around three administrative levels:
Super Admin: Platform-level management and oversight of organizations, users, and system operations.
Admin: Organization-level administration, employee management, document oversight, and visibility into employee conversations.
Employee: Access to assigned organizational knowledge, document uploads, and AI-powered conversations.
4. Centralized Conversation Management
Employees can maintain their own AI conversations.
Admins can review employee chat histories for organizational oversight.
Centralized visibility into how teams interact with company knowledge.
Structured conversation and user management.
5. Document & Media Management
Cloudinary-powered document and asset management.
Organized document handling and storage.
Integration between document processing and the retrieval pipeline.
Technical Architecture
Built with a custom backend-driven architecture, combining modern web technologies with production-oriented AI infrastructure.
AI Architecture: Custom RAG pipeline
Vector Search: MongoDB Atlas Vector Search
Database: MongoDB
File Management: Cloudinary
AI Integration: LLM APIs and embedding models
Communication: Server-Sent Events (SSE)
Backend: Node.js
Frontend: React-based application
The Engineering Behind It
The core challenge was not simply connecting an LLM to a document database. It was designing a structured knowledge retrieval system that could support multiple users within an organization while maintaining role-based administration and centralized oversight.
The solution combines document ingestion, intelligent chunking, vector embedding, semantic retrieval, context construction, and streamed AI generation into one integrated workflow.
The Outcome
KnowledgeFlow transforms scattered organizational documents into an interactive knowledge system, helping teams find information faster, reduce repetitive internal queries, and access company knowledge through natural conversations.
Built to make organizational knowledge searchable, accessible, and genuinely useful.
0 likes · 1 view · Nov 10, 2026
Built an enterprise AI knowledge management platform using a custom RAG architecture. Enables multiple employees to upload company documents, retrieve contextua
