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AWS Software / SaaS Global

Bynder

Enterprise Knowledge Search

Bynder initially acquired a company to incorporate visual search capabilities into its software. The fast rate of customer adoption encouraged Bynder to build on its success.

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One of Bynder’s customers states that the time spent searching for assets for a typical campaign task has decreased by 75%, resulting in a big boost in productivity for the team.
The search results are also deeper and more accurate, returning approximately 50 percent more options on average.
Through its solution for digital asset management, Bynder helps over 4,000 companies globally store, organize, and distribute over 175 million assets, totaling 18 PB of data.
more options returned on average per search 50%

Reported impact

Business outcome

Bynder’s generative AI–powered search has transformed how its customers interact with their digital assets. At the same time, the company protects the privacy and intellectual property rights of its customers’ digital content, as their data is not used to train the large language model. Key reported metrics include reduction in search time 75%; more options returned on average per search 50%; One of Bynder’s customers states that the time spent searching for assets for a typical campaign task has decreased by 75%, resulting in a big boost in productivity for the team..

  • Customer Experience
  • Productivity Improvement

Solution

Learn how digital-asset-management company Bynder improved search using Amazon Titan Multimodal Embeddings on Amazon Bedrock. Through its solution for digital asset management, Bynder helps over 4,000 companies globally store, organize, and distribute over 175 million assets, totaling 18 PB of data. For digital asset management users, content findability is of paramount importance, and any improvements in the speed and accuracy of search are a significant benefit. As a longtime customer of Amazon Web Services (AWS), Bynder chose to implement visual-similarity search that is powered by Amazon Titan Multimodal Embedding s in Amazon Bedrock , which provides an easy way to build and scale generative artificial intelligence (AI) applications. Using this solution, Bynder transformed how its customers discover and use their digital assets. Source data enters the Bynder workflow through its application services, processed with Amazon Titan Multimodal Embeddings, Amazon Bedrock.

Data flow

Source data enters the Bynder workflow through its application services, processed with Amazon Titan Multimodal Embeddings, Amazon Bedrock.

Solution architecture

2 components · 1 layer
  1. Compute
    • Amazon Titan Multimodal Embeddings Converts images and text into vectors for multimodal search and retrieval.
    • Amazon Bedrock Hosts the foundation models and inference APIs that power the generative AI features.

Architecture clues

  • The solution converts images into vectors using Titan Multimodal Embeddings so that customers can find assets by selecting similar images or describing what they’re looking for in natural language.
  • Then, both the search terms and the stored images are converted into vectors to identify matches according to visual and contextual similarity.
  • “The setup using Amazon Bedrock made it very easy to get those vectors because it was a very clear API,” says Bankras.

Evidence from the source

Bynder initially acquired a company to incorporate visual search capabilities into its software. The fast rate of customer adoption encouraged Bynder to build on its success.
One of Bynder’s customers states that the time spent searching for assets for a typical campaign task has decreased by 75%, resulting in a big boost in productivity for the team.
The search results are also deeper and more accurate, returning approximately 50 percent more options on average.
The solution converts images into vectors using Titan Multimodal Embeddings so that customers can find assets by selecting similar images or describing what they’re looking for in natural language.
Through its solution for digital asset management, Bynder helps over 4,000 companies globally store, organize, and distribute over 175 million assets, totaling 18 PB of data.
Using Amazon Bedrock, Bynder extended its comprehensive AI-powered search capabilities in its software.
Transparency Evidence & methodology 90/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    Bynder customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/bynder-bedrock-case-study/
    Source type
    Vendor-published customer story
    Published
    Jan 28, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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