Enterprise-Grade Document Intelligence Platform powered by RAG, Multi-Agent Orchestration, and Local LLMs.
QUANDOC AI is an advanced AI-powered document intelligence system designed to understand, retrieve, analyze, compare, and reason over uploaded documents using Retrieval-Augmented Generation (RAG).
The system combines semantic retrieval, hybrid search pipelines, reranking strategies, orchestration workflows, memory-aware conversations, and local LLM inference to create a production-style enterprise AI architecture.
Unlike traditional chatbot demos, QUANDOC AI focuses on:
- grounded AI responses
- hallucination reduction
- retrieval quality
- intelligent orchestration
- enterprise-style reasoning workflows
- Advanced RAG Architecture
- Hybrid Retrieval (Semantic + BM25)
- Pinecone Vector Database Integration
- Multi-Agent Workflow Orchestration
- LangGraph AI Flows
- Query Rewriting Pipeline
- Context-Aware Memory System
- Reranking Pipeline
- Hallucination Control
- Local LLM Inference using Ollama
- PDF Document Intelligence
- Conversational Document QA
- Enterprise-Style AI Reasoning
- FastAPI
- Python
- Uvicorn
- LangChain
- LangGraph
- Ollama
- Phi3 / Llama3 / Mistral
- Nomic Embeddings
- Pinecone
- Hybrid Retrieval
- BM25
- Semantic Search
- Reranking Pipelines
- Retrieval-Augmented Generation (RAG)
- Multi-Agent Systems
- AI Orchestration
- Query Rewriting
- Context Grounding
- Hallucination Reduction
- Memory-Aware Conversations
- Workflow Routing
- Intelligent Context Synthesis
User Query
↓
Query Rewriter
↓
Hybrid Retriever
(BM25 + Semantic Search)
↓
Reranker
↓
Context Builder
↓
LangGraph Orchestrator
↓
AI Agent Workflow
↓
LLM Response Generation
↓
Grounded Enterprise Response
backend/
│
├── app/
│ ├── api/
│ ├── rag/
│ ├── workflows/
│ ├── agents/
│ ├── services/
│ └── main.py
│
├── uploads/
├── requirements.txt
└── .envgit clone https://github.com/yourusername/quandoc-ai.git
cd quandoc-ai/backendpython -m venv venvvenv\Scripts\activatesource venv/bin/activatepip install -r requirements.txtCreate a .env file:
PINECONE_API_KEY=your_api_key
PINECONE_INDEX=quandoc-aiDownload:
https://ollama.com
Pull models:
ollama pull phi3
ollama pull nomic-embed-textuvicorn app.main:app --reloadServer:
http://127.0.0.1:8000
Swagger Docs:
http://127.0.0.1:8000/docs
Use:
/upload- Upload research paper or enterprise document
Example JSON:
{
"question": "Explain the hybrid retrieval architecture discussed in the document."
}{
"question": "Summarize the main architecture discussed across multiple sections."
}{
"question": "Compare this section with the previous concept discussed earlier."
}{
"question": "What does the document say about quantum blockchain insurance?"
}Expected:
- system should refuse unsupported answers
While building QUANDOC AI, several real-world AI engineering problems were addressed:
- hallucinated responses
- retrieval inconsistency
- vector database integration
- reranking quality
- workflow orchestration failures
- context fragmentation
- dependency conflicts
- memory handling
- retrieval latency optimization
QUANDOC AI was built as a hands-on AI engineering system to deeply understand:
- production-grade RAG systems
- enterprise AI orchestration
- retrieval pipelines
- LLM reliability
- system-level AI debugging
- multi-agent workflow design
The project reinforced an important lesson:
Real AI engineering is learned by building systems that break — and solving the problems behind them.
- Streaming responses
- Citation generation
- Multi-modal document support
- OCR pipelines
- Advanced agent routing
- Redis memory layer
- Kubernetes deployment
- Authentication & RBAC
- Real-time collaboration
- Evaluation & observability dashboards
Uzain Mohid
AI Engineering • RAG Systems • GenAI Workflows • Enterprise AI Architecture