Bynder Reduces Search Time by 75% Using Amazon Bedrock with Amazon Titan Multimodal Embeddings
BynderAmazon Bedrock · Amazon Titan Multimodal EmbeddingsBynder, a digital asset management company serving over 4,000 companies globally and storing more than 175 million assets (18 PB of data), built visual-similarity search using Amazon Titan Multimodal Embeddings in Amazon Bedrock. The solution converts images and search queries into vectors to match by visual and contextual similarity. One Bynder customer reports that time spent searching for assets for a typical campaign task decreased by 75%, and search results return approximately 50% more relevant options on average. Bynder does not use customer data to train the underlying large language model. The company is now exploring frame-by-frame video indexing.
Overview
Bynder, a digital asset management company serving over 4,000 companies globally and storing more than 175 million assets (18 PB of data), built visual-similarity search using Amazon Titan Multimodal Embeddings in Amazon Bedrock. The solution converts images and search queries into vectors to match by visual and contextual similarity. One Bynder customer reports that time spent searching for assets for a typical campaign task decreased by 75%, and search results return approximately 50% more relevant options on average. Bynder does not use customer data to train the underlying large language model. The company is now exploring frame-by-frame video indexing.
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Inspect the highlighted sourceThe challenge
For digital-asset-management users, content findability is critical, and any improvement in the speed and accuracy of search delivers significant value; Bynder, which helps over 4,000 companies globally store, organize and distribute more than 175 million assets totaling 18 PB of data, sought to extend its AI-powered search capabilities to improve how customers discover and use their digital assets.
The solution
Bynder implemented visual-similarity search powered by Amazon Titan Multimodal Embeddings in Amazon Bedrock. The solution converts both images and text search queries into vectors, matching assets by visual and contextual similarity, letting customers find assets by selecting similar images or describing what they're looking for in natural language.
Reported business value
One Bynder customer reports that time spent searching for assets for a typical campaign task decreased by 75%, and search results return approximately 50% more relevant options on average. The solution scales effortlessly across customers' massive asset libraries with virtually no limitations on image quantity, and Bynder does not use customer data to train the underlying large language model.
Sources
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