Search and Retrievalwith Hybrid RAG
Fusioni combines keyword search, semantic search and vector similarity into a single retrieval pipeline. Agents pull the right content from your sources and generate answers grounded in real data—not guesswork.
AI Search console
Retrieving grounded context
"What is the best treatment for lower back pain?"
0.95
Top score
3
Sources
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Search time
Evidence-Based Treatment for Lower Back Pain
vectorStudies show that a combination of exercise therapy, manual therapy and patient education produces the best outcomes for chronic lower back pain...
Lower Back Pain: Clinical Guidelines 2024
hybridThe latest clinical guidelines recommend a multimodal approach including physical therapy, cognitive behavioral therapy and medication management...
Exercise Protocols for Lumbar Spine Rehabilitation
keywordTargeted exercises for core stability, flexibility and strength show measurable improvement in pain reduction and functional outcomes...
Hybrid Search
Combines BM25 keyword matching with dense vector retrieval. Better coverage across both precise terminology queries and open-ended natural language questions—without having to choose one or the other.
Vector Search
Uses embeddings to find content by meaning, not just words. Supports multiple embedding models—you can assign different models per collection or use case.
Multi-Modal RAG
Retrieves from text, images, PDFs and structured sources. Results are fed directly to the LLM as context, with chunking, reranking and filtering applied before answer generation.
Metadata and filters
Use source, date, collection, category, access rules, and custom metadata to narrow retrieval and keep answers tied to the right business context.
How AI Search works
From scattered content to trusted answers.
AI Search is the retrieval layer behind reliable agents and assistant experiences. It indexes your content, retrieves the most relevant chunks, reranks them, and gives the LLM the evidence it needs to answer with sources.
Ingest content and metadata
Connect documents, product data, website pages, knowledge bases, PDFs, and structured records with metadata and access context.
Create searchable representations
Chunk content, generate embeddings, keep keyword indexes, and preserve source references for citations and traceability.
Retrieve, filter, and rerank
Combine keyword and vector results, apply metadata filters, rerank by relevance, and select the strongest evidence.
Generate grounded responses
Pass retrieved context to agents or chat experiences so answers are based on approved content, not model memory alone.
Built for real retrieval problems.
Search quality depends on more than embeddings. Fusioni AI Search treats retrieval as a full product surface: source control, metadata, scoring, chunking, access, and answer grounding all work together.
Precise control over retrieval
Tune filters, collections, scoring, similarity thresholds, and reranking rules for each use case or agent.
Source-aware answers
Keep references to the original content so teams can inspect where an answer came from and improve the knowledge base.
Ready for agents and apps
Use AI Search inside internal tools, customer assistants, support workflows, research copilots, and Fusioni Platform journeys.