Evidence Retrieval AI
Multi-agent evidence retrieval system with supervisor-routed specialist delegation. Multi-stage retrieval pipeline across hybrid vector search, LLM-scored relevance ranking, and quality-weighted synthesis with inline evidence metadata. Retraction-filtered retrieval with citation traversal across 3 external evidence APIs, 3072-dimensional embedding space, and consensus detection across specialist outputs.
A clinician asking "what does the evidence say?" shouldn't get one model's guess from one source. ClinicalSearch retrieves across multiple evidence bases, filters out retracted work, and shows where every claim came from. Multi-agent evidence retrieval system with domain-specialized agents. A supervisor routes queries by LLM reasoning to relevant specialists; each runs a multi-stage pipeline — hybrid vector search, external API fallback, retraction filtering, LLM reranking, and quality-weighted sort. Evidence quality metadata propagates inline; consensus detection identifies agreement, debate, and emerging findings across specialist outputs.
Evidence synthesis across heterogeneous domain-specific corpora requires concurrent retrieval from multiple specialized sources, quality assessment at retrieval time, and attribution-preserving synthesis. Single-model retrieval flattens domain distinction and lacks the multi-source aggregation needed to surface contradictions.
Supervisor-mediated specialist delegation routes each query by LLM reasoning. Each specialist runs a multi-stage retrieval pipeline with hybrid semantic search, external API calls with local databases, citation traversal, and LLM-based relevance scoring that drops low-quality chunks before synthesis. Consensus detection operates across specialist outputs to surface agreement, debate, and emerging findings.
