Intelligent Retrieval
Retrieval That Thinks Before It Searches
Five layers of intelligence between your question and your answer.
Query Understanding
We Don't Search What You Typed. We Search What You Meant.
Query Decomposition
Example:
"Compare our Q3 revenue to competitors"
What we search:
- 1Our company Q3 revenue figures
- 2Competitor Q3 revenue reports
- 3Revenue gap analysis factors
HyDE (Hypothetical Document Embeddings)
Instead of searching for your question, we generate what the perfect answer would look like — then find documents that match THAT.
Query Expansion
Example:
"AR status"
What we search:
- 1Accounts Receivable status
- 2Customer invoice payments
- 3Outstanding balances
Multi-Strategy Retrieval
Three Search Methods. One Intelligent Result.
Vector Search
Semantic understanding of meaning, not just keywords.
Keyword Search
Exact term matching for precision when needed.
Knowledge Graph
Relationship traversal for connected information.
Reciprocal Rank Fusion
Intelligently merges results from all strategies.
Intelligent Reranking
Finding Documents Is Easy. Finding RELEVANT Documents Is Hard.
Cross-Encoder Reranking
Every query-document pair scored by a specialized model. Not just "similar words" — actual relevance assessment.
LLM Reranking
For complex queries, an LLM evaluates each result: "Does this actually answer the question?"
Corrective RAG
When Retrieval Fails, We Don't. We Fix It.
Automatic Validation
Every retrieved document graded for relevance. Confidence score calculated before response.
Self-Healing Retrieval
- High confidence → Proceed with answer
- Ambiguous → Refine query, re-retrieve
- Low confidence → Fall back to web search
Guardrails
Protection Built In, Not Bolted On
Input Guardrails
- Prompt injection detection
- PII scrubbing from queries
- Blocked topic enforcement
Output Guardrails
- Hallucination detection
- PII leak prevention
- Citation verification
- Toxicity filtering
Experience Intelligent Retrieval
See how these five layers work together with your actual documents.