Why KDB-X is the next step for real-time data and AI in capital markets

Capital markets infrastructure is under pressure.

Data volumes continue to scale. Architectures are becoming harder to maintain. And AI is no longer experimental—it’s moving into production, where latency, consistency, and trust matter.

In this session, Ashok Reddy and Mike Gilfix discuss how firms are rethinking their approach to real-time data and AI—and why many are moving away from fragmented stacks toward a unified platform.

What you’ll learn

• Why traditional architectures struggle to support real-time and AI workloads at scale
• How fragmented systems introduce latency, complexity, and operational risk
• What changes when streaming, historical, and AI workloads run on a single platform
• How KDB-X reduces time-to-value while maintaining performance and control
• The role of GPU acceleration in moving AI from experimentation to production
• How firms are enabling real-time, AI-driven workflows across trading, risk, and operations
  Many firms already recognize the gap between research, production, and AI initiatives—but few have addressed the root cause: disconnected infrastructure.

Who should attend

• CTOs, CIOs, and CDOs modernizing trading infrastructure
• Heads of Quant, Electronic Trading, and Data Engineering
• Quant developers, data scientists, and platform engineers
• Existing kdb users evaluating their next step
• Firms exploring AI-driven trading and analytics workflows

Register here