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Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
A retrieval strategy that combines keyword-based search (BM25, full-text) with semantic vector search to find the most relevant documents for an AI agent's response. Keyword search catches exact matches (error codes, product names, policy numbers) that semantic search misses, while semantic search handles paraphrased queries and conceptual similarity. Fusing both approaches typically improves retrieval accuracy by 15–30% compared to either alone.
See it as a workflow
Support Ticket Deflection WorkflowTrigger, steps, n8n nodes, guardrails and an importable template — plus what it costs to have it built.
Or skip the build
Workflows from $197/month, custom agents from $2,000.