How Northwestern University built a multilingual generative AI search tool with AWS | Amazon Web Services
Search experiences break down quickly when content grows more complex and multilingual. This customer story blog post shows how Northwestern University Libraries rebuilt search using generative AI on AWS to better connect users with rich text, audio, and visual content. Read the story to see why the team chose AWS for flexibility, cost transparency, and fast iteration, then reach out to Silicon Network to talk about applying similar approaches to your AI search or discovery projects.
What is the purpose of the multilingual AI search tool at Northwestern University?
Northwestern University developed the multilingual generative AI search tool to enhance accessibility and usability of its extensive digital collections. The tool aims to provide a more intuitive search experience for users, allowing them to find multimedia content more easily, especially for those who may not be familiar with traditional search methods.
Why did Northwestern University choose AWS for their AI search tool?
Northwestern University chose AWS for its flexibility, cost transparency, and ability to support rapid iteration. As an early adopter of AWS, the university found that AWS services like Amazon Bedrock and Amazon OpenSearch Service provided a scalable and resilient foundation for their AI search tool.
How does the AI search tool improve user experience?
The AI search tool improves user experience by enabling concept-based searches rather than relying solely on keywords. It supports multilingual queries, provides contextual explanations for search results, and surfaces content related to underrepresented topics, making the collections more approachable and easier to navigate for all users.
How Northwestern University built a multilingual generative AI search tool with AWS | Amazon Web Services
published by Silicon Network
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