Contextual RAG
The next evolution in AI knowledge retrieval. Contextual RAG adds semantic understanding to every chunk of your data, delivering 67% more accurate responses.
"The quarterly revenue increased by 15% compared to the previous period."
"This chunk is from Acme Corp's Q3 2024 Financial Report, specifically from the Revenue Analysis section. The quarterly revenue increased by 15% compared to the previous period."
Why Traditional RAG Falls Short
Standard RAG systems split documents into chunks and embed them directly. This loses critical context about where each piece of information comes from.
Traditional RAG
Context-BlindChunks don't know which document they belong to
"The revenue increased" - whose revenue? When?
Can't distinguish outdated from current data
Semantic similarity ≠ actual relevance
Contextual RAG
Context-AwareSource, section, date, and author travel with chunk
LLM adds explanatory prefix to each chunk
Knows which data is most recent and relevant
Best of keyword and semantic search combined
The Contextual RAG Pipeline
Four intelligent steps that transform your knowledge base into highly accurate AI responses
Document Ingestion
Upload PDFs, docs, websites, or API data. We extract text while preserving structure and metadata.
Smart Chunking
Intelligent splitting by semantic boundaries, not arbitrary character limits. Maintains meaning integrity.
Context Generation
AI analyzes each chunk and generates a contextual prefix explaining its origin and meaning.
Hybrid Indexing
Dual BM25 + vector embeddings for both keyword precision and semantic understanding.
Why Contextual RAG Matters
Tangible improvements that translate to better AI experiences
Dramatically Fewer Hallucinations
With full context available, AI makes fewer incorrect assumptions and generates more factually accurate responses.
Higher Retrieval Precision
Context-enriched chunks rank higher when actually relevant, surfacing the right information more consistently.
Temporal Intelligence
AI understands when information was created and prioritizes recent, relevant data over outdated content.
Source Attribution
Every response can cite exactly where information came from - document, section, and page number.
Multi-Source Synthesis
Intelligently combines information from multiple documents while maintaining source clarity.
Domain Specialization
Context includes domain-specific terminology and relationships unique to your industry.
Under the Hood
Advanced technology stack powering Contextual RAG
Core Architecture
Vector Database
Pinecone/Qdrant with HNSW indexing for sub-50ms retrieval at any scale
Embedding Models
OpenAI text-embedding-3-large or custom fine-tuned models for your domain
BM25 Keyword Index
Elasticsearch-powered keyword matching for exact term precision
Context Generator
Claude/GPT-4 generates rich contextual prefixes during indexing
Advanced Features
Get Contextual RAG Today
Contextual RAG is automatically enabled for all Business subscribers. No additional setup required - just upload your documents and experience the difference.
Business Plan Results
Frequently Asked Questions
Common questions about Contextual RAG
Traditional RAG simply splits documents into chunks and embeds them. Contextual RAG adds an AI-generated explanation to each chunk before embedding, providing information about the document source, section, date, and relevance. This context dramatically improves retrieval accuracy.
Yes, initial ingestion takes slightly longer because we generate context for each chunk. However, queries are just as fast (under 100ms), and the significant improvement in accuracy more than compensates for the extra processing time.
We support PDF, DOCX, XLSX, PPTX, TXT, HTML, Markdown, and more. You can also ingest entire websites, Notion pages, Google Docs, Confluence, and connect to APIs for real-time data synchronization.
Absolutely. Your data is stored in isolated, encrypted vector databases. We're SOC 2 Type II certified, GDPR compliant, and offer HIPAA-compliant configurations for healthcare. Your documents are never used to train our models.
Yes! We integrate with Zendesk Guide, Intercom Articles, Notion, Confluence, SharePoint, Google Drive, and more. We can also migrate existing vector embeddings from other systems and add contextual enhancement.
Experience the Contextual RAG difference
See how much more accurate your AI can be with context-aware knowledge retrieval.
14-day free trial • Business plan features • No credit card required