Nasdaq Boosts AI Performance with NVIDIA NeMo Retriever and NIM
Nasdaq enhances its AI platform with NVIDIA NeMo Retriever and NIM, achieving faster insights and smarter investment decisions.
Financial Services
How Nasdaq Is Driving Faster Insights and Smarter Investment Decisions with Scalable AI Innovation
Nasdaq, a leader in capital markets since 1971, has built an AI platform to improve performance, accuracy, and cost efficiency. The platform leverages NVIDIA NeMo™ Retriever and NVIDIA NIM™ microservices, part of NVIDIA AI Enterprise, to enhance generative AI capabilities. This results in faster and more accurate retrieval tasks, reduced expenses, and optimized resource usage.
Key Achievements:
- 30% Faster Response Times: Enhanced processing and retrieval capabilities speed up interactions.
- 30% Improved Accuracy: Chatbots and conversational interfaces see a significant boost in accuracy.
- Real-Time Feedback: NVIDIA NIM provides real-time performance insights, enabling swift issue resolution.
Using AI to Enhance Operations, Services, and Products
Nasdaq’s AI strategy focuses on improving both internal operations and external products. The platform integrates AI at each product level to enhance functionality and user experience. Michael O’Rourke, senior vice president and head of AI and emerging technology at Nasdaq, emphasized AI’s role in unifying data across businesses and technologies to build better products and services.
Global Hackathons Accelerate AI Platform Development
Nasdaq conducted four global hackathons to test the platform’s potential. These events generated hundreds of hacks, providing valuable feedback and accelerating development. The hackathons highlighted the need for faster data indexing and improved accuracy, leading to a 30% improvement in speed and better results.
Future Use Cases
Nasdaq plans to expand its AI platform across its vast data ecosystem, which includes over 160 petabytes of data. Future use cases include:
- NVIDIA NeMo Retriever extraction: Offloading bespoke regulatory pipeline processes.
- Multimodal processing: Handling text and image data more efficiently.
- GPU-based vector store: Optimizing large dataset handling with NVIDIA RAPIDS™ cuVS-accelerated vector stores.
Related News
AI in Finance Progresses but Faces Delays Before Widespread Use
Examining the current state of AI adoption in financial services reveals progress but significant delays before widespread client-facing applications are ready.
Kaizen AI Generators Power Continuous Improvement in Tech
Jan Bosch explains how kaizen AI generators enable systems to continuously adapt and improve through real-time monitoring and experimentation.
About the Author
Dr. Emily Wang
AI Product Strategy Expert
Former Google AI Product Manager with 10 years of experience in AI product development and strategy formulation. Led multiple successful AI products from 0 to 1 development process, now provides product strategy consulting for AI startups while writing AI product analysis articles for various tech media outlets.