RELEVANCE
Personalized search, unparalleled relevance
With powerful search relevance, Elastic provides all the tools you need to build AI-powered search experiences that help users find exactly what they need.
Learn about cutting-edge methods for hybrid search and advanced reranking strategies such as Learning to Rank (LTR) and cross-encoders.
Watch webinarSee how easy it is to get started with setting up Elasticsearch.
See quick start videoJina embeddings and rerankers deliver high-precision GPU-accelerated inference across 30+ languages for search, RAG, and AI agents.
Explore Jina search modelsAI-POWERED RELEVANCE
Developer tools for generative AI and semantic search
Use industry leading advanced relevance ranking features like BM25F for lexical search, native semantic search with Jina AI models, and hybrid search using reciprocal rank fusion (RRF) to enter a new era of contextual relevance.
ELASTIC INFERENCE SERVICE
Fast, scalable inference for AI workflows
Run GPU-accelerated inference natively in Elasticsearch with Elastic Inference Service (EIS), delivering fast, multilingual Jina AI embeddings and rerankers on managed GPUs and an expanded catalog of ready-to-use third-party models for real-world agent workflows.
Reranking
The most relevant search engine for RAG
Rerankers apply machine learning models to fine-tune your search results and bring the most relevant results to the top based on user preferences and signals. Jina rerankers enable fast multilingual reranking directly in Elasticsearch for RAG and agentic workflows without added infrastructure.
QUERY RULES AND SYNONYMS API
Optimize search performance
Provide customizable instructions through metadata for more control of search results in response to targeted queries. Query rules in Elasticsearch help you promote high-priority content to end users for specific use cases. Simplify organizing and updating related words for website searches using the synonyms management API.
Fine-tune your search relevance model
Elasticsearch query language supports advanced search techniques (full-text, sparse/dense vector search), along with hybrid search using reciprocal rank fusion (RRF) or Jina AI reranker models. Combine this with filtering, boosting, and rescoring methods, and you're able to further fine-tune your search relevance model, customizing to your needs.
HYPER-RELEVANCE
Harness the power of machine learning
Whether you're adding new concepts to broaden the impact of your search or seeking new ways to improve search accuracy, machine learning can augment search and business insights to enhance your search applications and customer experience. Improve semantic relevance with generative AI, vector search, support for NLP transformer models, and third-party model management.
