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How Valor Intelligent Processing Uses Speech Analytics to Improve CFPB Compliance and Agent Performance

Provana

For the last few years, collection agencies have been using call center speech analytics to help reduce delinquencies, mitigate losses, and maximize their accounts receivable recovery. Having said that, only malleable speech analytics solutions that quickly evolve as per customer preferences lead to better collection yield.

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Amazon SageMaker Feature Store now supports cross-account sharing, discovery, and access

AWS Machine Learning

SageMaker Feature Store now makes it effortless to share, discover, and access feature groups across AWS accounts. With this launch, account owners can grant access to select feature groups by other accounts using AWS Resource Access Manager (AWS RAM).

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Supercharge your AI team with Amazon SageMaker Studio: A comprehensive view of Deutsche Bahn’s AI platform transformation

AWS Machine Learning

At Deutsche Bahn, a dedicated AI platform team manages and operates the SageMaker Studio platform, and multiple data analytics teams within the organization use the platform to develop, train, and run various analytics and ML activities. For high availability, multiple identical private isolated subnets are provisioned.

APIs 108
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How to Measure & Improve Call Center Average Speed of Answer

Callminer

Be aware of this, and make sure to account for the effect of outliers when drawing conclusions from the measurement. . Speech analytics is one technology that cannot only assess ASA and other performance metrics, it can also detect issues with IVR routing and identify additional routing options. . Customer Abandonment.

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Build a receipt and invoice processing pipeline with Amazon Textract

AWS Machine Learning

One area that holds significant potential for improvement is accounts payable. On a high level, the accounts payable process includes receiving and scanning invoices, extraction of the relevant data from scanned invoices, validation, approval, and archival. The second step (extraction) can be complex. An AWS Cloud9 environment.

APIs 99
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Streamline RAG applications with intelligent metadata filtering using Amazon Bedrock

AWS Machine Learning

In some use cases, particularly those involving complex user queries or a large number of metadata attributes, manually constructing metadata filters can become challenging and potentially error-prone. The extracted metadata is used to construct an appropriate metadata filter. model in Amazon Bedrock.

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Call Center Agent Feedback: Tips & Best Practices for Providing Effective Agent Feedback

Callminer

Tools like interaction analytics can help call center managers identify relevant issues and deliver precise, targeted feedback to agents and have a more direct impact on metrics like call handling time. Leverage analytics to offer targeted agent training and coaching. With a global team of 1,000 agents who handle more than 5.5