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Generative AI has transformed customer support, offering businesses the ability to respond faster, more accurately, and with greater personalization. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences.
Amazon Bedrock announces the preview launch of Session Management APIs, a new capability that enables developers to simplify state and context management for generative AI applications built with popular open source frameworks such as LangGraph and LlamaIndex. Building generative AI applications requires more than model API calls.
Personalized outbound communication can be a powerful tool to increase user engagement and conversion. To achieve this, you can use Amazon Personalize to generate user-personalized recommendations and Amazon Bedrock to generate the text of the email.
These documents are internally called account plans (APs). In 2024, this activity took an account manager (AM) up to 40 hours per customer. In this post, we showcase how the AWS Sales product team built the generative AI account plans draft assistant. Its a game-changer for serving my full portfolio of accounts.
The custom Google Chat app, configured for HTTP integration, sends an HTTP request to an API Gateway endpoint. Before processing the request, a Lambda authorizer function associated with the API Gateway authenticates the incoming message. The following figure illustrates the high-level design of the solution.
It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. Incoming requests to the gateway go through this point.
After you set up the connector, you can create one or multiple data sources within Amazon Q Business and configure them to start indexing emails from your Gmail account. The connector supports authentication using a Google service account. We describe the process of creating an account later in this post. Choose Create.
Prerequisites Before you start, make sure you have the following prerequisites in place: Create an AWS account , or sign in to your existing account. topicPolicyConfig={ 'topicsConfig': [ { 'name': 'In-Person Tutoring', 'definition': 'Requests for face-to-face, physical tutoring sessions.', 'examples': [ 'Can you tutor me in person?'
We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.
Prerequisites Before creating your application in Amazon Bedrock IDE, you’ll need to set up a few resources in your AWS account. This includes setting up Amazon API Gateway , AWS Lambda functions, and Amazon Athena to enable querying the structured sales data. Navigate to the AWS Secrets Manager console and find the secret -api-keys.
Businesses use their data with an ML-powered personalization service to elevate their customer experience. Amazon Personalize accelerates your digital transformation with ML, making it easier to integrate personalized recommendations into existing websites, applications, email marketing systems, and more.
Prerequisites Before proceeding, make sure that you have the necessary AWS account permissions and services enabled, along with access to a ServiceNow environment with the required privileges for configuration. AWS Have an AWS account with administrative access. For more information, see Setting up for Amazon Q Business. Choose Next.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Weve seen our sales teams use this capability to do things like consolidate meeting notes from multiple team members, analyze business reports, and develop account strategies. Earlier this year, we published the first in a series of posts about how AWS is transforming our seller and customer journeys using generative AI.
The protection of personally identifiable information (PII), protected health information (PHI), and confidential business data is crucial because this information flows through RAG systems. For more information, see Redacting PII entities with asynchronous jobs (API). The entities to mask can be configured using RedactionConfig.
Reduced time and effort in testing and deploying AI workflows with SDK APIs and serverless infrastructure. We can also quickly integrate flows with our applications using the SDK APIs for serverless flow execution — without wasting time in deployment and infrastructure management.
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon through a single API, along with a broad set of capabilities to build generative AI applications with security, privacy, and responsible AI.
Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that’s best suited for your use case. The deployment will output the API Gateway endpoint URL and an API key.
These steps might involve both the use of an LLM and external data sources and APIs. Agent plugin controller This component is responsible for the API integration to external data sources and APIs. The LLM agent is an orchestrator of a set of steps that might be necessary to complete the desired request.
Amazon Personalize allows you to add sophisticated personalization capabilities to your applications by using the same machine learning (ML) technology used on Amazon.com for over 20 years. You can also add data incrementally by importing records using the Amazon Personalize console or API. No ML expertise is required.
Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.
Many CX, marketing and operations leaders are asking how they can use customer journey orchestration to deliver better, more personalized experiences that will improve CX and business outcomes, like retention, customer lifetime value and revenue. Journey orchestration goes beyond traditional personalization techniques.
The solution uses the FMs tool use capabilities, accessed through the Amazon Bedrock Converse API. This enables the FMs to not just process text, but to actively engage with various external tools and APIs to perform complex document analysis tasks. For more details on how tool use works, refer to The complete tool use workflow.
TR’s customers span across the financial, risk, legal, tax, accounting, and media markets. The key requirement for TR’s new machine learning (ML)-based personalization engine was centered around an accurate recommendation system that takes into account recent customer trends. Solution architecture.
With personalized content more likely to drive customer engagement, businesses continuously seek to provide tailored content based on their customer’s profile and behavior. Orchestrating the Amazon Personalize batch inference jobs using AWS Step Functions. We walk through the end-to-end solution without a single line of code.
Additionally, Cropwise AI enables personalized recommendations at scale, tailoring seed choices to align with local conditions and specific farm needs, creating a more precise and accessible selection process. It facilitates real-time data synchronization and updates by using GraphQL APIs, providing seamless and responsive user experiences.
We use various AWS services to deploy a complete solution that you can use to interact with an API providing real-time weather information. There is also memory retention across the interaction allowing a more personalized user experience. This Lambda function used a weather API to fetch up-to-date meteorological data.
Intelligent document processing , translation and summarization, flexible and insightful responses for customer support agents, personalized marketing content, and image and code generation are a few use cases using generative AI that organizations are rolling out in production.
Solution overview For organizations processing or storing sensitive information such as personally identifiable information (PII), customers have asked for AWS Global Infrastructure to address these specific localities, including mechanisms to make sure that data is being stored and processed in compliance with local laws and regulations.
Today, we are excited to announce Promotions feature in Amazon Personalize that allows you to explicitly recommend specific items to your users based on rules that align with your business goals. You can use promotions in domain dataset groups and custom dataset groups ( User-Personalization and Similar-Items recipes).
Beyond Amazon Bedrock models, the service offers the flexible ApplyGuardrails API that enables you to assess text using your pre-configured guardrails without invoking FMs, allowing you to implement safety controls across generative AI applicationswhether running on Amazon Bedrock or on other systemsat both input and output levels.
Handling Basic Inquiries : Chat GPT can assist with basic inquiries such as order status, account information, shipping details, or product specifications. Personalized Recommendations : Based on customer preferences and purchase history, Chat GPT can provide personalized product or service recommendations.
Furthermore, it might contain sensitive data or personally identifiable information (PII) requiring redaction. Step Functions orchestrates AWS services like AWS Lambda and organization APIs like DataStore to ingest, process, and store data securely. The fetched data is put into an S3 data store bucket for processing.
Personalization has become a cornerstone of delivering tangible benefits to businesses and their customers. We present our solution through a fictional consulting company, OneCompany Consulting, using automatically generated personalized website content for accelerating business client onboarding for their consultancy service.
Although RAG excels at real-time grounding in external data and fine-tuning specializes in static, structured, and personalized workflows, choosing between them often depends on nuanced factors. We first provided a detailed walkthrough on how to fine-tune, host, and conduct inference with customized Amazon Nova through the Amazon Bedrock API.
In this post, we show how to use the user’s current device type as context to enhance the effectiveness of your Amazon Personalize -based recommendations. Although this post shows how Amazon Personalize can be used for a video on demand (VOD) use case, it’s worth noting that Amazon Personalize can be used across multiple industries.
According to a McKinsey study , 78% of consumers are more likely to make repeat purchases from companies that provide personalized experiences. Send the text, images, and metadata to Amazon Bedrock using its API to generate embeddings using the Amazon Titan Multimodal Embeddings G1 model. An OpenSearch Service domain.
Until recently, organizations hosting private AWS DeepRacer events had to create and assign AWS accounts to every event participant. This often meant securing and monitoring usage across hundreds or even thousands of AWS accounts. Build a solution around AWS DeepRacer multi-user account management.
When creating a scene of a person performing a sequence of actions, factors like the timing of movements, visual consistency, and smoothness of transitions contribute to the quality. Add team members using their email addresses—they will receive instructions to set up their accounts.
As attendees circulate through the GAIZ, subject matter experts and Generative AI Innovation Center strategists will be on-hand to share insights, answer questions, present customer stories from an extensive catalog of reference demos, and provide personalized guidance for moving generative AI applications into production.
Amazon Personalize is excited to announce the new Next Best Action ( aws-next-best-action ) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion. All your data is encrypted to be private and secure.
Generative AI technology, such as conversational AI assistants, can potentially solve this problem by allowing members to ask questions in their own words and receive accurate, personalized responses. After a successful authentication, a REST API hosted on API Gateway is invoked.
The Amazon Bedrock single API access, regardless of the models you choose, gives you the flexibility to use different FMs and upgrade to the latest model versions with minimal code changes. Amazon Titan FMs provide customers with a breadth of high-performing image, multimodal, and text model choices, through a fully managed API.
Challenges with traditional onboarding The traditional onboarding process for banks faces challenges in the current digital landscape because many institutions don’t have fully automated account-opening systems. This constraint impacts the flexibility for customers to initiate account opening at their preferred time.
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