The conversation around generative AI (GenAI) has largely centered on its ability to process unstructured and semi-structured data, like text, images, audio, and video. However, as GenAI applications evolve to meet the complex demands of enterprise use cases—ranging from cybersecurity and network troubleshooting to comprehensive business intelligence—there is an urgent need to integrate real-time and tabular data into the equation. Achieving this requires a robust real-time retrieval engine capable of fusing insights from both structured and unstructured data. This often involves executing advanced analytics, including spatial, time series, graph, and OLAP processing, alongside vector search capabilities.
Join us for this insightful webinar to discover how real-time retrieval is revolutionizing GenAI applications. Learn about the innovations that are enabling these breakthroughs and explore how Kinetica is uniquely positioned to support these enterprise-scale demands in a secure, scalable manner.
Tune in on Nov. 12th at 1:00 PM ET to:
- Discover the challenges of integrating real-time text and tabular data into sophisticated GenAI applications.
- Learn how Kinetica delivers exceptional performance for vector search and ad-hoc SQL query execution on real-time data
- Explore essential features like data catalogs, query federation, and language-to-SQL capabilities that simplify building advanced GenAI applications for enterprise needs.
Speakers
Nima Negahban
Nima Negahban is the CEO and co-founder of Kinetica, where he pioneered the use of GPUs for real-time analytics, starting with tracking terrorists for the Department of Defense. He evolved Kinetica into a cutting-edge, distributed SQL database that unifies vector, graph, spatial, and time-series analytics for instant insights across multiple real-time datasets. Today, Kinetica is trusted by top industries like telecom, automotive, and defense.
Doug Henschen
Doug Henschen is Vice President and Principal Analyst at Constellation Research, focusing on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen’s research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies.
