RAG.lu: Supercharge Your Document Intelligence
Our Retrieval-Augmented Generation platform transforms how organizations extract insights and knowledge from vast document collections.
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Understanding Retrieval-Augmented Generation
RAG technology combines the power of large language models with your organization's proprietary data for accurate, contextual, and trustworthy AI responses.
- 1
Document Processing
RAG.lu ingests various document formats (PDFs, Word, text) and transforms them into searchable knowledge vectors while preserving relationships.
- 2
Intelligent Retrieval
When a query is received, our system efficiently retrieves the most relevant document sections from your knowledge base.
- 3
Contextual Generation
The AI model generates responses based on both the retrieved context and its general knowledge, ensuring accuracy and relevance.
Powerful Features for Document Intelligence
RAG.lu provides a comprehensive suite of tools for transforming how you interact with document repositories.
Multi-Format Document Support
Process various document types including PDF, Word, PowerPoint, Excel, HTML, Markdown, and plain text with advanced OCR capabilities.
Semantic Search Engine
Find information based on meaning, not just keywords, with our advanced vector-based semantic search technology.
Automatic Summarization
Generate concise, accurate summaries of documents, sections, or entire collections with adjustable detail levels.
Natural Language Q&A
Ask questions in plain English and get precise answers with citations to source documents for verification.
Visual Document Analytics
Visualize document relationships, topic clusters, and knowledge gaps through interactive dashboards and charts.
API Integration
Seamlessly integrate RAG.lu's capabilities into your existing applications and workflows through our comprehensive API.
Technical Architecture
Built on cutting-edge technologies to ensure performance, scalability, and security.
Core Components
- Document Processor: Handles parsing, chunking, and preprocessing of various document formats
- Vector Database: Stores and indexes document embeddings for efficient semantic retrieval
- LLM Orchestrator: Manages prompts, context assembly, and response generation
- API Layer: RESTful and GraphQL interfaces for integration with external systems
Technology Stack
- LangChain: For orchestrating document processing and embedding pipelines
- Vector databases: Support for Pinecone, Weaviate, Milvus, and Chroma
- LLM Support: OpenAI, Anthropic, Cohere, HuggingFace, and custom models
- Containerization: Docker and Kubernetes for deployment and scaling
Industry Applications
RAG.lu delivers transformational capabilities across a wide range of industries and use cases.
Legal Document Analysis
Extract insights from contracts, case law, and regulatory documents with precision and contextual understanding.
- 70% faster contract review process
- 85% accuracy in clause identification
Technical Knowledge Base
Transform product documentation, support manuals, and technical guides into an intelligent support system.
- 60% reduction in support ticket volume
- 45% faster issue resolution time
Research & Intelligence
Analyze research papers, reports, and market data to extract insights and identify emerging trends.
- 80% faster literature review process
- 50% increase in knowledge discovery
Performance Metrics
RAG.lu delivers measurable improvements in document processing and knowledge extraction.
90%
Accuracy Rate
In extracting and retrieving relevant information
10x
Faster Processing
Compared to manual document analysis methods
65%
Cost Reduction
In knowledge management operational expenses
1M+
Documents
Can be processed and indexed per day
Ready to Transform Your Document Intelligence?
Get started with RAG.lu today and unlock the insights hidden in your document repositories.