Deploy a serverless workflow that automatically generates SEO-optimized titles, descriptions, and features from raw product data. Focus on product strategy while AI handles content enrichment.
Overview
This Guidance demonstrates how retailers can use generative AI to improve their product catalog management process. By automating the traditionally manual task of processing and standardizing product data from manufacturers, retailers can significantly enhance their operational efficiency. The Guidance addresses the common challenge of handling diverse data formats from multiple manufacturers, transforming this time-consuming process into a streamlined, automated workflow. This approach not only enhances productivity but also helps ensure more consistent and higher-quality product catalog entries, enabling retailers to maintain accurate and up-to-date product information more effectively.
Benefits
Accelerate product listing creation
Enhance customer search experience
Improve product discoverability with AI-enriched catalog data in OpenSearch Serverless. Drive higher conversion rates through more relevant search results and detailed product information.
Scale catalog operations cost-effectively
Leverage serverless architecture that adapts to your business demand with pay-as-you-go pricing. Eliminate infrastructure management overhead while maintaining high performance during peak periods.
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 product's raw data, which was created or integrated from your marketplace, is sent individually in a JSON format using Amazon API Gateway.
Step 2
API Gateway calls an AWS Step Functions workflow, which then invokes four AWS Lambda functions in sequence, using the raw data as parameter. Each function will enrich the data.
Step 3
The first Lambda function uses the raw data to invoke Amazon Bedrock to generate an improved title.
Step 4
The second Lambda function uses the raw data to generate an enriched description, considering best practices in search engine optimization and highlighting the product's benefits.
Step 5
The third Lambda function extracts features commonly used by ecommerce companies. These might include relevant characteristics like material, color, size, weight, dimensions, capacity, compatibility, or functionality.
Step 6
The last Lambda function creates a JSON file with the product's title, description, and feature list. It then inserts the data into Amazon OpenSearch Serverless.
Step 7
OpenSearch Serverless acts as an enhanced product catalog for ecommerce. It applies generative AI to its listings to enhanced search results and improve the customer experience.
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.