Deploy parallel AI agents that simultaneously analyze demand forecasts, competitive intelligence, and margin compliance. Reduce pricing analysis time while improving decision accuracy through coordinated machine learning insights.
Overview
This Guidance demonstrates how to transform complex retail pricing decisions into data-driven recommendations by coordinating specialized AI agents that analyze demand forecasts, competitor pricing, and profit margins. Three AI agents work in parallel—one forecasting market demand using historical data, another monitoring real-time competitor pricing, and a third ensuring profitability targets are met. A supervisor agent coordinates these specialized agents through AWS Step Functions, with Amazon Bedrock powering the AI capabilities and Amazon SageMaker Canvas handling demand predictions. You can reduce pricing analysis from days to minutes while maintaining final control over all pricing decisions, enabling faster market response with greater confidence.
Benefits
Accelerate pricing decisions with AI
Scale pricing operations serverlessly
Leverage AWS Lambda and Step Functions to automatically handle fluctuating workloads without infrastructure management. Focus your team on strategic pricing while AWS manages the underlying compute resources.
Maintain compliance with automated guardrails
Implement real-time margin compliance checks using specialized AI agents that validate pricing recommendations against your business rules. Prevent pricing errors before they impact revenue or customer relationships.
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
The Pricing Analyst accesses the React frontend application served through AWS CloudFront. They select a product from the catalog that they want to price.
Step 2
Authentication is handled by Amazon Cognito, providing secure user login and session management. Static web application assets are stored in Amazon Simple Storage Service (Amazon S3).
Step 3
Real-time subscriptions and GraphQL queries flow through AWS AppSync.
Step 4
API requests route through Amazon API Gateway (REST) to backend services.
Step 5
The Orchestrator Lambda receives requests and triggers AWS Step Functions to coordinate workflows.
Step 6
Step Functions invokes three specialized AWS Lambda functions in parallel, each implementing a custom agent using Strands framework: Demand Forecasting, Competitive Intelligence, and Margin Compliance.
Step 7
Agents leverage Amazon Bedrock, a fully managed service for generative AI applications with foundation models from leading AI companies, and SageMaker Canvas for demand forecasting.
Step 8
Pricing data is persisted in DynamoDB; model artifacts and training data are stored in Amazon S3 buckets.
Step 9
CloudWatch provides monitoring and EventBridge handles event-driven communication between services.
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.