Enable store associates to instantly access product information and store procedures through voice or text queries, resulting in fast and consistent customer assistance.
Overview
This Guidance shows how to enhance workforce management through AI-driven automation and real-time intelligence. It demonstrates how to provide staff with quick access to relevant insights, enabling more informed decision-making. The system helps managers adjust schedules efficiently, while supporting employees in personalizing customer interactions based on current information. Through intelligent automation, this Guidance shows how to improve workforce allocation, increase operational flexibility, and enhance the customer experience, leading to improved responsiveness and efficiency in operations.
Benefits
Accelerate customer service delivery
Streamline store task management
Intelligently assign task by analyzing inventory levels, KPIs, and staff availability. Improve operational efficiency through data-driven workforce coordination.
Enhance operational knowledge sharing
Deploy an intelligent knowledge management system that provides consistent, policy-compliant responses to workforce queries. Improve decision-making with centralized organizational expertise.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Step 1
A store associate interacts with the application through a UI which uses Amazon Route 53 for DNS resolution and AWS Certificate Manager (ACM) for SSL/TLS certificate management.
Step 2
Amazon CloudFront leverages its global edge network to deliver static content from Amazon Simple Storage Service (Amazon S3) and dynamic content from Amazon Elastic Kubernetes Service (Amazon EKS), ensuring fast, low-latency access to web applications worldwide.
Step 3
Amazon Cognito manages user authentication and authorization for secure access to the web application.
Step 4
All the data is encrypted at rest using AWS Key Management Service (AWS KMS).
Step 5
An application load balancer (ALB) forwards REST API and web socket traffic to backend service in Amazon EKS through the corresponding Amazon EKS Ingress Controller.
Step 6
The Amazon EKS cluster manages the deployment, scaling, and orchestration of the containerized backend service. The container images are stored in Amazon Elastic Container Registry (Amazon ECR).
Step 7
The Amazon Bedrock Nova Sonic model processes voice inputs and generates natural-sounding responses while seamlessly connecting to backend systems through function calling. This enables sophisticated speech-to-speech interactions for business applications. Function calling enables models to interact with external systems by identifying when to invoke specific functions, generating structured JSON parameters, and incorporating returned data into responses.
Step 8
Application data, such as employee or product data, is stored in Amazon DynamoDB and fetched and updated based on the interactions using the tools.
Step 9
Amazon Bedrock Knowledge Bases vectorizes and stores organizational content (such as FAQs, SOPs, employment policies) in Amazon OpenSearch Service vector store. It enables AI-powered recommendations grounded in existing organizational knowledge. This supports hands-free retrieval augmented generation (RAG) applications through voice and text.
Step 10
The Guidance can integrate with third-party systems and existing business applications through standardized APIs defined as tools.
Deploy with confidence
Everything you need to launch this Guidance in your account is right here.
Let's make it happen
Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.