TraceDocs Assistant
We developed an AI-powered chatbot that transforms static document repositories into a dynamic, queryable knowledge base. The system retrieves information from updated SOPs and other documents in real time using natural language queries, enabling employees to access accurate, context-aware responses instantly while reducing search effort and improving compliance.
What We Built
A fully automated document retrieval and chatbot ecosystem, including:
- RAG-based architecture for retrieval-augmented generation
- Elastic search and ChromaDB for efficient indexing and semantic search
- Hugging Face Transformers and GPT models for precise query comprehension
- Document ingestion pipeline for detecting new or updated content
- React.js frontend with an intuitive conversational interface
- Django backend with secure access control and role-based permissions
- Azure cloud deployment for scalable, enterprise-ready infrastructure
The system acts as an intelligent retrieval engine, dynamically ingesting, indexing, and serving relevant information from SOPs and document repositories as updates occur.
Key Features
RAG-Powered Chatbot
- Retrieval-augmented generation for accurate, context-aware responses
- Embedding and efficient querying of complex document structures
- Responses linked to specific document sections for traceability
Dynamic Document Updates
- Continuous detection of new or updated documents
- Automated ingestion and re-indexing for real-time information availability
Intelligent Query Processing
- Natural language understanding for precise question interpretation
- Context-aware semantic retrieval with source attribution
- Multi-turn conversations with context retention
Intuitive Front with Secure Backend
- User-friendly React.js interface for natural language queries
- Robust Django backend with authentication and role-based access control
- Encrypted storage and compliance-ready data handling
How It Works
Document updates or new SOPs are detected and ingested into Elastic search
Content is chunked, indexed, and made ready for semantic search
User submits a natural language query through the chatbot interface
System retrieves relevant content and RAG model generates a concise, accurate response
Chatbot delivers the answer with direct links to specific sections, retaining context for follow-ups
Results
80%
reduction in search time for SOP and document information
90%
accuracy in retrieving relevant sections with traceable sources
Real-time
responses under 2 seconds
Seamless integration
with SharePoint and other enterprise document repositories
Improved
compliance and procedural accuracy
Value Dilevered
This project transformed static and frequently updated document repositories into a dynamic, intelligent knowledge interface. By combining real-time document ingestion, semantic search, and RAG-based AI, employees can instantly access accurate information, reduce manual search effort, improve compliance, and accelerate decision-making across the organization.
Want to build AI-powered Problem solutions?
#AI Chatbot #Document Retrieval #RAG #Natural Language Processing #SharePoint Integration #Knowledge Management #Elasticsearch #Hugging Face #Workflow Automation