Enterprise Knowledge Search
They consistently use our insights to enhance their services, allowing us to stay focused on our core product.” As large language models (LLMs) emerged, Dovetail began improving its products to automate processes to improve efficiency, such as identifying key moments in transcripts and classifying support tickets. Recognizing the potential of LLMs, Dovetail saw an opportunity to develop new features to enhance data aggregation and pattern recognition across customer communication channels.
Reported impact
By using Amazon Bedrock, Dovetail maintains a rapid pace of innovation while controlling costs. “Amazon Bedrock's serverless design boosts our product development speed,” Wooden explains. Key reported metrics include day or less to create prototypes 1; weeks to develop new generative AI features 2; hours saved by customers each week on data analysis 10.
Dovetail Enhances Customer Insights with New Generative AI Products Developed in Weeks Using Amazon Bedrock Dovetail is a global B2B SaaS company whose products help teams leverage customer insights. To improve scalability and automate data processing and analysis, Dovetail sought to leverage large language models (LLMs) and build its generative AI capabilities. By adopting Amazon Bedrock, Dovetail can now create prototypes within a day and release new generative AI products and features in just two weeks. Building on AWS ensures Dovetail maintains data security, residency, and low latency while delivering intuitive AI-driven tools that enhance customer insights. When AWS introduced Amazon Bedrock , a fully managed service offering a range of high-performing foundation models, Dovetail was invited to join the service preview cohort. Source data is ingested through Amazon Transcribe, processed with Amazon Bedrock, Amazon SageMaker.
Source data is ingested through Amazon Transcribe, processed with Amazon Bedrock, Amazon SageMaker.
By deploying a range of Anthropic Claude models, including Claude 3.5 Sonnet on Amazon Bedrock, Dovetail launched new Magic features to enhance data analysis.
By using Amazon Bedrock, Dovetail maintains a rapid pace of innovation while controlling costs.
Previously, Dovetail had used Amazon SageMaker to deploy deep learning models but needed a more flexible solution for its variable workloads.
They consistently use our insights to enhance their services, allowing us to stay focused on our core product.” As large language models (LLMs) emerged, Dovetail began improving its products to automate processes to improve efficiency, such
When AWS introduced Amazon Bedrock , a fully managed service offering a range of high-performing foundation models, Dovetail was invited to join the service preview cohort.
“Amazon Bedrock's serverless design boosts our product development speed,” Wooden explains.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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