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Elevate healthcare interaction and documentation with Amazon Bedrock and Amazon Transcribe using Live Meeting Assistant

AWS Machine Learning

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. By using the solution, clinicians don’t need to spend additional hours documenting patient encounters. This blog post focuses on the Amazon Transcribe LMA solution for the healthcare domain.

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.

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How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

AWS Machine Learning

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

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How to hold customers accountable, and other outcomes based customer success best practices

ChurnZero

1: To hold customers accountable for outcomes, practice “proactive reactivity” Q: How do you hold customers accountable? Keishla : Accountability in business relationships is key. 4: Build a strong feedback loop between your customer success and product teams.

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Dialogue-guided intelligent document processing with foundation models on Amazon SageMaker JumpStart

AWS Machine Learning

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. The system is capable of processing images, large PDF, and documents in other format and answering questions derived from the content via interactive text or voice inputs.

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Build an automated insight extraction framework for customer feedback analysis with Amazon Bedrock and Amazon QuickSight

AWS Machine Learning

Extracting valuable insights from customer feedback presents several significant challenges. Scalability becomes an issue as the amount of feedback grows, hindering the ability to respond promptly and address customer concerns. Large language models (LLMs) have transformed the way we engage with and process natural language.

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11 Best Practices for Managing Customer Feedback

Nicereply

Maximize the value of using Nicereply day-to-day and learn how to manage customer feedback! If you work as a Customer Support Manager, working with feedback is a huge part of your to-do list. Let’s look at the best practices of how to manage customer feedback. You can export your report as a CSV document.