Intelligent Document
Question & Answer System

Leveraging AI to unlock insights from your enterprise data

The Business Challenge

Information Retrieval at Scale

  • Manual document search consumes valuable time
  • Traditional keyword search lacks contextual understanding
  • Critical insights buried in extensive documentation
  • Knowledge fragmented across multiple repositories

A more efficient approach is required

The Solution: RAG Technology

Retrieval-Augmented Generation

An AI-powered system that combines intelligent document retrieval with natural language generation to deliver precise, contextual answers from your organization's knowledge base.

In practical terms:

The system indexes your documents, understands semantic context, and provides accurate responses to natural language queries—drawing exclusively from your proprietary data.

Process Overview

1

Document Ingestion

Upload documents in multiple formats (PDF, Word, text, code)

2

AI Processing

System analyzes and indexes content for semantic search

3

Query & Retrieve

Ask questions in natural language, receive accurate answers

System Architecture

Backend

Python Core

AI processing engine
Document indexing & retrieval

⟷
Frontend

Ruby on Rails

Web interface
User authentication & history

Modular, scalable architecture with clear separation of concerns

Technical Workflow

graph TD A[Documents] -->|Chunking| B[Text Segments] B -->|Embedding Model| C[Vector Embeddings] C -->|Store| D[(Vector Database)] E[User Query] -->|Embedding Model| F[Query Vector] F -->|Similarity Search| D D -->|Retrieve Top K| G[Relevant Segments] G -->|Context| H[Prompt Assembly] E -->|Question| H H -->|Combined Input| I[Large Language Model] I -->|Generate| J[Answer] style D fill:#dbeafe style I fill:#dbeafe style J fill:#dcfce7

Technology Stack

Backend (Python)

  • LlamaIndex: RAG framework
  • FastEmbed: Local embeddings
  • ChromaDB: Vector storage
  • FastAPI: REST API server

Frontend (Ruby)

  • Rails 8.1: Web framework
  • Hotwire: Real-time UI
  • Tailwind CSS: Interface design
  • SQLite: User data store

AI Infrastructure

  • OpenRouter: Model gateway
  • GPT-4, Claude, Gemini support
  • Flexible model selection

Infrastructure

  • Redis: Job queue
  • Background workers
  • Asynchronous processing

Enterprise Applications

Research & Development

Rapidly search through technical papers, research documentation, and patent filings

Legal & Compliance

Query contracts, regulatory documents, and compliance materials efficiently

Technical Documentation

Access information from codebases, API documentation, and technical specifications

Knowledge Management

Centralize and query organizational knowledge, procedures, and best practices

Key Benefits

  • Data Privacy: Documents processed and stored on-premises
  • Cost Efficiency: Free document processing; pay-per-query model
  • Time Savings: Instant answers instead of manual document review
  • Accuracy: Responses grounded in your specific documentation
  • Accessibility: Intuitive interface requiring no technical expertise
  • Flexibility: Support for multiple document formats and sources

Getting Started

Deploy intelligent document search for your organization

Implementation Steps:

  1. Configure and deploy Python backend services
  2. Set up Rails frontend application
  3. Index initial document repository
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