Vespa is a full-stack AI search platform that combines vector search, full-text retrieval, and structured data queries with real-time machine-learned ranking and inference. It is purpose-built to support complex applications requiring high performance and scalability, such as retrieval-augmented generation (RAG), recommendation systems, and personalized search experiences. Vespa's architecture allows for seamless scaling, handling from thousands to billions of data items while maintaining low latency. The platform supports tensor-based operations, enabling the handling of complex data structures and enhancing its capability to deliver accurate and relevant search results. Vespa is optimized for high-throughput environments, making it suitable for enterprise-level applications that demand consistent performance under heavy query loads.

Key Features

• Unified platform combining vector, text, and structured data search

• Real-time machine-learned ranking and inference

• Support for retrieval-augmented generation (RAG) and recommendation systems

• Tensor-based operations for complex data structures

• Scalable architecture handling from thousands to billions of data items

• Optimized for high-throughput environments with low latency

• Extensible and flexible for custom application development

• Proven in production by industry leaders like Perplexity and Spotify

Industries

• E-commerce

• Financial Services

• Health & Life Sciences

• Market Intelligence

• Travel and Hospitality

• Media and Entertainment

Vespa is ideal for organizations seeking to implement advanced search and recommendation systems at scale. E-commerce platforms can utilize Vespa to provide personalized product recommendations based on user behavior and preferences. Financial institutions can leverage Vespa for real-time fraud detection by analyzing transaction data. Healthcare providers can use Vespa to enable semantic search across medical records, improving information retrieval efficiency. Media companies can apply Vespa to deliver context-aware content recommendations, enhancing user engagement. Travel agencies can utilize Vespa to offer personalized travel suggestions by analyzing user profiles and preferences. Educational institutions can use Vespa to recommend learning materials tailored to individual student needs. Research organizations can leverage Vespa to enable efficient search across large datasets, facilitating data analysis. Government agencies can apply Vespa to analyze public data for policy-making decisions. Non-profit organizations can use Vespa to analyze donor data and tailor fundraising campaigns. Vespa's flexibility and scalability make it suitable for a wide range of applications across various industries.