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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.
Unclear ROI ChatGPT is currently not accessible via API and the cost of a (hypythetical) API call are unclear. They are empowering enterprises to deliver exceptional service that is instant, personalized, in any language, and on any channel.
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.
Clone the repo To get started, clone the repository by running the following command, and then switch to the working directory: git clone [link] Build your guardrail To build the guardrail, you can use the CreateGuardrail API. There are multiple components to a guardrail for Amazon Bedrock.
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.
Today, we are excited to announce three launches that will help you enhance personalized customer experiences using Amazon Personalize and generative AI. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users.
Furthermore, these notes are usually personal and not stored in a central location, which is a lost opportunity for businesses to learn what does and doesn’t work, as well as how to improve their sales, purchasing, and communication processes. With Lambda integration, we can create a web API with an endpoint to the Lambda function.
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. Import the API schema from the openapi_schema.json file that you downloaded earlier. Download all three sample data files.
To support small businesses on their brand-building journey, VistaPrint provides customers with personalized product recommendations, both in real time on vistaprint.com and through marketing emails. Transform the data to create Amazon Personalize training data. Import bulk historical data to train Amazon Personalize models.
Personalization can improve the user experience of shopping, entertainment, and news sites by using our past behavior to recommend the products and content that best match our interests. You can also apply personalization to conversational interactions with an AI-powered assistant.
We provide the service account with authorization scopes to allow access to the required Gmail APIs. After you create the project, on the navigation menu, choose APIs and Services and Library to view the API Library. On the API Library page, search for and choose Admin SDK API. Choose Enable to enable this API.
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.
The sports industry has long been shaped by innovation, with brands constantly pushing boundaries to enhance performance and personalization. Personalization has also become a growing trend, allowing players to customize their gear, such as golf balls, with unique text.
This could be APIs, code functions, or schemas and structures required by your end application. Tool use with Amazon Nova To illustrate the concept of tool use, we can imagine a situation where we provide Amazon Nova access to a few different tools, such as a calculator or a weather API. Amazon Nova will use the weather tool.
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.
ServiceNow Obtain a ServiceNow Personal Developer Instance or use a clean ServiceNow developer environment. This involves creating an OAuth API endpoint in ServiceNow and using the web experience URL from Amazon Q Business as the callback URL. The other fields are automatically generated by the ServiceNow OAuth server.
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.
Research shows that the most impactful communication is personalized—showing the right message to the right user at the right time. According to McKinsey , “71% of consumers expect companies to deliver personalized interactions.” How exactly do Amazon Personalize and Amazon Bedrock work together to achieve this?
LotteON aims to be a platform that not only sells products, but also provides a personalized recommendation experience tailored to your preferred lifestyle. The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway.
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.
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.
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.
It’s like having your own personal travel agent whenever you need it. By using advanced AI technology and Amazon Location Service , the trip planner lets users translate inspiration into personalized travel itineraries. Amazon Bedrock is the place to start when building applications that will amaze and inspire your users.
When complete, a notification chain using Amazon Simple Queue Service (Amazon SQS) and our internal notifications service API gateway begins delivering updates using Slack direct messaging and storing searchable records in OpenSearch for future reference.
Delivering personalized news and experiences to readers can help solve this problem, and create more engaging experiences. However, delivering truly personalized recommendations presents several key challenges: Capturing diverse user interests – News can span many topics and even within specific topics, readers can have varied interests.
Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.
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.
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.
With this launch, you can programmatically run notebooks as jobs using APIs provided by Amazon SageMaker Pipelines , the ML workflow orchestration feature of Amazon SageMaker. Furthermore, you can create a multi-step ML workflow with multiple dependent notebooks using these APIs.
Hundreds of software as a service (SaaS) applications are being developed around these pre-trained models, which are either directly served to end-customers, or fine-tuned first on a per-customer basis to generate personal and unique content (such as avatars, stylized photo edits, video game assets, domain-specific text, and more).
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.
Improving document retrieval results helps personalize the responses generated for each user. invoke(inputs["query"])) ) return retrieval_chain Option 2: Access the underlying Boto3 API The Boto3 API is able to directly retrieve with a dynamic retrieval_config.
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.
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.
Our sales, marketing, and operations teams use Field Advisor to brainstorm new ideas, as well as generate personalized outreach that they can use with their customers and stakeholders. Amazon Q Business makes these features available through the service API, allowing for a customized look and feel on the frontend.
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.
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.
Amazon Bedrock enables access to powerful generative AI models like Stable Diffusion through a user-friendly API. One of its primary applications lies in advertising and marketing, where it can be used to create personalized ad campaigns and an unlimited number of marketing assets. This API will be used to invoke the Lambda function.
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.
78% of customers expect more personalization in interactions than ever before. As we look forward to 2025 and beyond, the expectation for a hyper-personalized and seamless experience will only intensify: consequently, now is the time for you to focus on what matters the most customer experience. What are Customer Engagement Platforms?
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