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Orchestrate an intelligent document processing workflow using tools in Amazon Bedrock

AWS Machine Learning

In this post, we focus on one such complex workflow: document processing. Rule-based systems or specialized machine learning (ML) models often struggle with the variability of real-world documents, especially when dealing with semi-structured and unstructured data.

APIs 102
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Enhance customer support with Amazon Bedrock Agents by integrating enterprise data APIs

AWS Machine Learning

Access to car manuals and technical documentation helps the agent provide additional context for curated guidance, enhancing the quality of customer interactions. The workflow includes the following steps: Documents (owner manuals) are uploaded to an Amazon Simple Storage Service (Amazon S3) bucket.

APIs 133
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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. LMA for healthcare is an extended version of the Live Meeting Assistant solution that has been adapted to generate clinical notes automatically during virtual doctor-patient consultations.

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Revolutionizing clinical trials with the power of voice and AI

AWS Machine Learning

Site monitors conduct on-site visits, interview personnel, and verify documentation to assess adherence to protocols and regulatory requirements. However, this process can be time-consuming and prone to errors, particularly when dealing with extensive audio recordings and voluminous documentation.

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Derive generative AI powered insights from Alation Cloud Services using Amazon Q Business Custom Connector

AWS Machine Learning

This post shows how to configure an Amazon Q Business custom connector and derive insights by creating a generative AI-powered conversation experience on AWS using Amazon Q Business while using access control lists (ACLs) to restrict access to documents based on user permissions. Who are the data stewards for my proprietary database sources?

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Generate training data and cost-effectively train categorical models with Amazon Bedrock

AWS Machine Learning

Lets say the task at hand is to predict the root cause categories (Customer Education, Feature Request, Software Defect, Documentation Improvement, Security Awareness, and Billing Inquiry) for customer support cases. We suggest consulting LLM prompt engineering documentation such as Anthropic prompt engineering for experiments.

Education 115
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Automate chatbot for document and data retrieval using Agents and Knowledge Bases for Amazon Bedrock

AWS Machine Learning

This post presents a solution for developing a chatbot capable of answering queries from both documentation and databases, with straightforward deployment. For documentation retrieval, Retrieval Augmented Generation (RAG) stands out as a key tool. Virginia) AWS Region. The following diagram illustrates the solution architecture.

Chatbots 134