Intelligent RAG Architecture

How Courdx Works

Most RAG systems do simple vector search: query in, documents out, hope for the best.

Courdx uses five layers of intelligence to deliver accurate, verifiable answers.

Question

Natural language query

Decompose

Break into sub-queries

Multi-Retrieve

Vector + Graph + Keyword

Validate

Check relevance

Cite

Sentence-level sources

Answer

Accurate response

The Complete Journey

From question to trustworthy answer in five intelligent steps.

Your Question

Natural language query from your team

Intelligent Query Processing

Query Decomposition → HyDE → Expansion → Routing

  • Complex questions broken into answerable parts
  • Generate what the perfect answer looks like
  • Expand acronyms and synonyms
  • Route to best retrieval strategy

Multi-Strategy Retrieval

Vector Search + Knowledge Graph + Keyword (Hybrid)

  • Semantic similarity via embeddings
  • Graph traversal for relationships
  • Keyword matching for precision
  • Reciprocal Rank Fusion combines results

Intelligent Validation

Relevance Grading → Reranking → Corrective Actions

  • Score every result for relevance
  • Cross-encoder reranking
  • Low confidence? Refine and retry
  • Fall back to web search if needed

Trustworthy Response

Answer + Citations + Confidence Score + Guardrails

  • Synthesize answer from validated sources
  • Cite every fact to exact sentence
  • Show confidence score
  • Apply output guardrails

Built on Research, Not Hype

Our retrieval pipeline implements state-of-the-art techniques from academic research and industry best practices.

Microsoft GraphRAG community detection
LlamaIndex Corrective RAG patterns
Cross-encoder reranking (ms-marco)
Reciprocal Rank Fusion for hybrid search
HyDE (Hypothetical Document Embeddings)
Query decomposition & expansion
Multi-vector retrieval strategies
Sentence-level citation tracking

See It In Action

Schedule a demo to see how Courdx processes your actual documents.