Category: HAQM Bedrock

In this post, we explore why Asure used the HAQM Web Services (AWS) post-call analytics (PCA) pipeline that generated insights across call centers at scale with the advanced capabilities of generative AI-powered services such as HAQM Bedrock and HAQM Q in QuickSight. Asure chose this approach because it provided in-depth consumer analytics, categorized call transcripts around common themes, and empowered contact center leaders to use natural language to answer queries. This ultimately allowed Asure to provide its customers with improvements in product and customer experiences.

Today, we’re excited to announce the general availability of HAQM Bedrock Data Automation, a powerful, fully managed capability within HAQM Bedrock that seamlessly transforms unstructured multimodal data into structured, application-ready insights with high accuracy, cost efficiency, and scalability.

In this blog post, we showcase a powerful solution that seamlessly integrates AWS generative AI capabilities in the form of large language models (LLMs) based on HAQM Bedrock into the Office experience. By harnessing the latest advancements in generative AI, we empower employees to unlock new levels of efficiency and creativity within the tools they already use every day.

Today, we’re announcing a significant enhancement to HAQM Bedrock Guardrails: AWS Identity and Access Management (IAM) policy-based enforcement. This powerful capability enables security and compliance teams to establish mandatory guardrails for every model inference call, making sure organizational safety policies are consistently enforced across AI interactions. This feature enhances AI governance by enabling centralized control over guardrail implementation.

In this post, we explore using HAQM Bedrock to create a text-to-SQL application using RAG. We use Anthropic’s Claude 3.5 Sonnet model to generate SQL queries, HAQM Titan in HAQM Bedrock for text embedding and HAQM Bedrock to access these models.

As the healthcare industry continues to embrace digital transformation, solutions that combine advanced technologies like audio-to-text translation and LLMs will become increasingly valuable in addressing key challenges, such as patient education, engagement, and empowerment. In this post, we discuss possible use cases for combining speech recognition technology with LLMs, and how the solution can revolutionize clinical trials.

Healthcare decisions often require integrating information from multiple sources, such as medical literature, clinical databases, and patient records. LLMs lack the ability to seamlessly access and synthesize data from these diverse and distributed sources. This limits their potential to provide comprehensive and well-informed insights for healthcare applications. In this blog post, we will explore how Mistral LLM on HAQM Bedrock can address these challenges and enable the development of intelligent healthcare agents with LLM function calling capabilities, while maintaining robust data security and privacy through HAQM Bedrock Guardrails.

Today, we’re announcing computer use support within HAQM Bedrock Agents using Anthropic’s Claude 3.5 Sonnet V2 and Anthropic’s Claude Sonnet 3.7 models on HAQM Bedrock. This integration brings Anthropic’s visual perception capabilities as a managed tool within HAQM Bedrock Agents, providing you with a secure, traceable, and managed way to implement computer use automation in your workflows.

This post focuses on RAG evaluation with HAQM Bedrock Knowledge Bases, provides a guide to set up the feature, discusses nuances to consider as you evaluate your prompts and responses, and finally discusses best practices. By the end of this post, you will understand how the latest HAQM Bedrock evaluation features can streamline your approach to AI quality assurance, enabling more efficient and confident development of RAG applications.

This post provides an overview of a custom solution developed for GoDaddy, a domain registrar, registry, web hosting, and ecommerce company that seeks to make entrepreneurship more accessible by using generative AI to provide personalized business insights to over 21 million customers. In this collaboration, the Generative AI Innovation Center team created an accurate and cost-efficient generative AI–based solution using batch inference in HAQM Bedrock, helping GoDaddy improve their existing product categorization system.