AI case study

NasdaqEnterprise data search

Slow embeddings bottlenecked search. NVIDIA NIM cut latency, driving 30% faster, more accurate insights across 160PB of data.

Published

Key results

Faster Response Times
30%
Accuracy Improvement
30%

Result highlights

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The story

Context

A global capital markets leader supporting over 130 marketplaces and managing 160 petabytes of data for 5,000 listed companies.

Challenge

Initial AI implementations suffered from high latency and escalating costs, particularly during slow embedding operations. The system struggled to...

Solution
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Quotes

“At Nasdaq, we are a technology platform company, and AI has the ability for us to unite all the different businesses and products. AI will help bring together data from all our businesses and technologies, and help us build better products and services.”

– Michael O’Rourke, Senior Vice President and Head of AI and Emerging Technology at Nasdaq, Nasdaq
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The company

Nasdaq logo

Nasdaq

nasdaq.com

Global exchange operator and technology provider for capital markets and trading.

IndustryFinancial Services
LocationNew York, NY, USA
Employees5K-10K
Founded1971

The vendor

NVIDIA logo

NVIDIA

www.nvidia.com

NVIDIA is a technology company that specializes in semiconductors, graphics processing units, and artificial intelligence for applications in data centers, gaming, and more.

IndustryTechnology
LocationSanta Clara, California, United States
Employees10K-50K
Founded1993

Use case

Nasdaq's Enterprise data search is part of this use case:

Enterprise Search
178 case studies(-9% YoY)
Proven impact?
LowModerateVery Strong
3.6Moderate
2.7Lowwithin Financial Services
4.2Moderatewithin Product Engineering
View 178 case studies for enterprise searchExplore enterprise search clusters

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