Remove Connections Remove Document Remove Effort Score
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Customer Experience Experimentation: Your Final Frontier

ECXO

Metrics such as Net Promoter Score (NPS), Customer Satisfaction (CSAT), and Customer Effort Score (CES) are commonly used. Document and Share Learnings : Share outcomes and insights across the organization to foster continuous improvement. Share Your Insights I’d love to hear your thoughts on CX and experimentation!

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Intelligent document processing with AWS AI services in the insurance industry: Part 1

AWS Machine Learning

The goal of intelligent document processing (IDP) is to help your organization make faster and more accurate decisions by applying AI to process your paperwork. Insurance customers can automate this process using AWS AI services to automate the document processing pipeline for claims processing. Part 2: Data enrichment and insights.

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Create Your CX Charter with These 6 Questions

Experience Investigators by 360Connext

A customer experience charter is a brief document outlining the agreements the CX governing team needs to align with their decisions. How Can We Prioritize CX efforts? Ultimately, your CX team is there to help with overall governance and prioritization for your CX efforts. The charter, however, is not a magical document.

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Build a generative AI assistant to enhance employee experience using Amazon Q Business

AWS Machine Learning

Customers like Deriv were successfully able to reduce new employee onboarding time by up to 45% and overall recruiting efforts by as much as 50% by making generative AI available to all of their employees in a safe way. You can then assign weights to document attributes after mapping them to index fields using the relevance tuning feature.

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Choosing the Best Text Analysis Software for Your Business

InMoment XI

Forrester states, “The XI platform’s strengths include knowledge-based/symbolic AI; genAI-based processes, including pre-processing and post-processing of data; document-level text mining; DevOps and text analytics embedded in other business applications; natural language understanding; and support for all relevant use cases.”

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Create a multimodal assistant with advanced RAG and Amazon Bedrock

AWS Machine Learning

Solution architecture The mmRAG solution is based on a straightforward concept: to extract different data types separately, you generate text summarization using a VLM from different data types, embed text summaries along with raw data accordingly to a vector database, and store raw unstructured data in a document store. split('.')[0]}.json"

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Improve LLM performance with human and AI feedback on Amazon SageMaker for Amazon Engineering

AWS Machine Learning

The team navigates a large volume of documents and locates the right information to make sure the warehouse design meets the highest standards. To increase training samples for better learning, we also used another LLM to generate feedback scores. This method addressed the RAG limitation and further improved the bot response quality.

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