The roadmap to scalable, real-time AI in financial services

What you'll learn

• Most enterprise AI initiatives in financial services aren't failing because of the model — they're failing because people aren’t using them because the answers are not in the context of the user. With 55% of financial services AI rollouts stalling before production, the context gap has become the defining obstacle between pilot and scale.

  • This ebook maps the path from fragmented, latency-prone data infrastructure to a production-ready context engine built for the demands of financial services. Inside you'll find:

  • Why context infrastructure—not model capability—is the real bottleneck blocking AI at scale

  • The three systemic risks driving deployment failures: legal liability, latency-driven fraud exposure, and hallucination from data blind spots

  • How a unified context engine replaces a fragmented stack of vector databases and custom glue code

  • Architecture patterns for fraud detection, global trading, and client advisory AI workflows

  • A reference architecture for deploying a Real-Time Context Engine on AWS

  • Built in partnership with AWS