Built-in vector database capabilities optimized for storing and processing AI embeddings, performing vector similarity searches, and creating embeddings for dense retrieval while capturing unstructured data meanings across multiple data formats
02
Search Ai Lake
Low-latency architecture optimized for AI operations, combining cloud-native services with object storage capabilities for data processing and search operations with automatic scaling and versionless deployment
03
Data Processing System
Supports hundreds of integrations for real-time data ingestion, enabling the processing of both structured and unstructured data from multiple sources with fine-tuning capabilities for search relevance optimization
04
Machine Learning Integration
Supports both supervised and unsupervised machine learning models, including the Elastic Learned Sparse Encoder for semantic search across domains without fine-tuning requirements
05
Hybrid Search Architecture
Combines textual, vector, hybrid, and semantic search techniques with native Learning to Rank capabilities for optimized search performance
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