Automating product description generation with HAQM Bedrock

In today’s ever-evolving world of ecommerce, the influence of a compelling product description cannot be overstated. It can be the decisive factor that turns a potential visitor into a paying customer or sends them clicking off to a competitor’s site. The manual creation of these descriptions across a vast array of products is a labor-intensive process, and it can slow down the velocity of new innovation. This is where HAQM Bedrock with its generative AI capabilities steps in to reshape the game. In this post, we dive into how HAQM Bedrock is transforming the product description generation process, empowering e-retailers to efficiently scale their businesses while conserving valuable time and resources.

Unlocking the power of generative AI in retail

Generative AI has captured the attention of boards and CEOs worldwide, prompting them to ask, “How can we leverage generative AI for our business?” One of the most promising applications of generative AI in ecommerce is using it to craft product descriptions. Retailers and brands have invested significant resources in testing and evaluating the most effective descriptions, and generative AI excels in this area.

Creating engaging and informative product descriptions for a vast catalog is a monumental task, especially for global ecommerce platforms. Manual translation and adaptation of product descriptions for each market consumes time and resources. This results in generic or incomplete descriptions, leading to reduced sales and customer satisfaction.

The power of HAQM Bedrock: AI-generated product descriptions

HAQM Bedrock is a fully managed service that simplifies generative AI development, offering high-performing foundation models (FMs) from leading AI companies. The Jurassic-2 series by AI21 Labs, Titan by HAQM, Claude by Anthropic, Command by Cohere, Llama 2 by Meta, and Stability by Stable Diffusion are seamlessly integrated into HAQM Bedrock, providing a diverse and powerful set of generative AI capabilities through a single API. It provides a comprehensive set of capabilities for building generative AI applications while ensuring privacy and security are maintained. With HAQM Bedrock, you can experiment with various FMs and customize them privately using techniques like fine-tuning and Retrieval Augmented Generation (RAG). The platform enables you to create managed agents for complex business tasks without the need for coding, such as booking travel, processing insurance claims, creating ad campaigns, and managing inventory.

For example, ecommerce platforms can initially generate basic product descriptions that include size, color, and price. However, HAQM Bedrock’s flexibility allows these descriptions to be fine-tuned to incorporate customer reviews, integrate brand-specific language, and highlight specific product features, resulting in tailored descriptions that resonate with the target audience. Moreover, HAQM Bedrock offers access to foundation models from HAQM and leading AI startups through an intuitive API, making the entire process seamless and efficient.

Using AI can have the following impact on the product description process:

• Faster approvals – Vendors experience a streamlined process, moving from product listing to approval in under an hour, eliminating frustrating delays
• Improved product listing velocity – When automated, your ecommerce marketplace sees a surge in product listings, offering consumers access to the latest merchandise nearly instantaneously
• Future-proofing – By embracing cutting-edge AI, you secure your position as a forward-looking platform ready to meet evolving market demands
• Innovation – This solution liberates teams from mundane tasks, allowing them to focus on higher-value work and fostering a culture of innovation

Solution overview

Before we dive into the technical details, let’s see the high-level preview of what this solution offers. This solution will allow you to create and manage product descriptions for your ecommerce platform. It empowers your platform to:

• Generate descriptions from text – With the power of generative AI, HAQM Bedrock can convert plain text descriptions into vivid, informative, and captivating product descriptions.
• Craft images – Beyond text, it can also craft images that align perfectly with the product descriptions, enhancing the visual appeal of your listings.
• Enhance existing content – Do you have existing product descriptions that need a fresh perspective? HAQM Bedrock can take your current content and make it even more compelling and engaging.

This solution is available in the AWS Solutions Library. We’ve provided detailed instructions in the accompanying README file. The README file contains all the information you need to get started, from requirements to deployment guidelines.

The system architecture comprises several core components:

• UI portal – This is the user interface (UI) designed for vendors to upload product images.
• HAQM Rekognition – HAQM Rekognition is an image analysis service that detects objects, text, and labels in images.
• HAQM Bedrock – Foundation models in HAQM Bedrock use the labels detected by HAQM Rekognition to generate product descriptions.
• AWS Lambda – AWS Lambda provides serverless compute for processing.
• Product database – The central repository stores vendor products, images, labels, and generated descriptions. This could be any database of your choice. Note that in this solution, all of the storage is in the UI.
• Admin portal – This portal provides oversight of the system and product listings, ensuring smooth operation. This is not part of the solution; we’ve added it for understanding.

The following diagram illustrates the flow of data and interactions within the system

The workflow includes the following steps:

1. The client initiates a request to the HAQM API Gateway REST API.
2. HAQM API Gateway passes the request to AWS Lambda through a proxy integration.
3. When operating on product image inputs, AWS Lambda calls HAQM Rekognition to detect objects in the image.
4. AWS Lambda calls LLMs hosted by HAQM Bedrock, such as the HAQM Titan language models, to generate product descriptions.
5. The response is passed back from AWS Lambda to HAQM API Gateway.
6. Finally, HTTP response from HAQM API Gateway is returned to the client.

Example use case

Imagine a vendor uploads a product image of shoes, and HAQM Rekognition identifies key attributes like “white shoes,” “sneaker,” and “durable.” The HAQM Bedrock Titan AI takes this information and generates a product description like, “Here is a draft product description for a canvas running shoe based on the product photo: Introducing the Canvas Runner, the perfect lightweight sneaker for your active lifestyle. This running shoe features a breathable canvas upper with leather accents for a stylish, classic look. The lace-up design provides a secure fit, while the padded tongue and collar add comfort. Inside, a removable cushioned insole supports and comforts your feet. The EVA midsole absorbs shock with each step, reducing fatigue. Flex grooves in the rubber outsole ensure flexibility and traction. With its simple, retro-inspired style, the Canvas Runner seamlessly transitions from workouts to everyday wear. Whether you’re running errands or running miles, this versatile sneaker will keep you moving in comfort and style.”

Design details

Let’s explore the components in more detail:

• User interface:
  • Front end – The front end of the vendor portal allows vendors to upload product images and displays product listings.
  • API calls – The portal communicates with the backend through APIs to process images and generate descriptions.
• HAQM Rekognition:
  • Image analysis – Triggered by API calls, HAQM Rekognition analyzes images and detects objects, text, and labels.
  • Label output – It outputs label data derived from the analysis.
• HAQM Bedrock:
  • NLP text generation – HAQM Bedrock uses the HAQM Titan natural language processing (NLP) model to generate textual descriptions.
  • Label integration – It takes the labels detected by HAQM Rekognition as input to generate product descriptions.
  • Style matching – HAQM Bedrock provides fine-tuning capabilities for HAQM Titan models to ensure that the generated descriptions match the style of the platform.
• AWS Lambda:
  • Processing – Lambda handles the API calls to services.
• Product database:
  • Flexible database – The product database is chosen based on customer preferences and requirements. Note this is not provided as part of the solution.

Additional capabilities

This solution goes beyond just generating product descriptions. It offers two more incredible options:

• Image and description generation from text – With the power of generative AI, HAQM Bedrock can take text descriptions and create corresponding images along with detailed product descriptions. Consider the potential:
  • Instantly visualizing products from text.
  • Automating image creation for large catalogs.
  • Enhancing customer experience with rich visuals.
  • Reducing content creation time and costs.
• Description enhancement – If you already have existing product descriptions, HAQM Bedrock can enhance them. Simply supply the text and the prompt, and HAQM Bedrock will skillfully enhance and enrich the content, rendering it highly captivating and engaging for your customers.

Conclusion

In the fiercely competitive world of ecommerce, staying at the forefront of innovation is imperative. HAQM Bedrock offers a transformative capability for e-retailers looking to enhance their product content, optimize their listing process, and drive sales. With the power of AI-generated product descriptions, businesses can create compelling, informative, and culturally relevant content that resonates deeply with customers. The future of ecommerce has arrived, and it’s driven by machine learning with HAQM Bedrock.

Are you ready to unlock the full potential of AI-powered product descriptions? Take the next step in revolutionizing your ecommerce platform. Visit the AWS Solutions Library and explore how HAQM Bedrock can transform your product descriptions, streamline your processes, and boost your sales. It’s time to supercharge your ecommerce with HAQM Bedrock!

About the Authors

Dhaval Shah is a Senior Solutions Architect at AWS, specializing in Machine Learning. With a strong focus on digital native businesses, he empowers customers to leverage AWS and drive their business growth. As an ML enthusiast, Dhaval is driven by his passion for creating impactful solutions that bring positive change. In his leisure time, he indulges in his love for travel and cherishes quality moments with his family.

Doug Tiffan is the Head of World Wide Solution Strategy for Fashion & Apparel at AWS. In his role, Doug works with Fashion & Apparel executives to understand their goals and align with them on the best solutions. Doug has over 30 years of experience in retail, holding several merchandising and technology leadership roles. Doug holds a BBA from Texas A&M University and is based in Houston, Texas.

Nikhil Sharma is a Solutions Architecture Leader at HAQM Web Services (AWS) where he and his team of Solutions Architects help AWS customers solve critical business challenges using AWS cloud technologies and services.

Kevin Bell is a Sr. Solutions Architect at AWS based in Seattle. He has been building things in the cloud for about 10 years. You can find him online as @bellkev on GitHub.

Nipun Chagari is a Principal Solutions Architect based in the Bay Area, CA. Nipun is passionate about helping customers adopt Serverless technology to modernize applications and achieve their business objectives. His recent focus has been on assisting organizations in adopting modern technologies to enable digital transformation. Apart from work, Nipun finds joy in playing volleyball, cooking and traveling with his family.

Marshall Bunch is a Solutions Architect at AWS helping North American customers design secure, scalable and cost-effective workloads in the cloud. His passion lies in solving age-old business problems where data and the newest technologies enable novel solutions. Beyond his professional pursuits, Marshall enjoys hiking and camping in Colorado’s beautiful Rocky Mountains.

Altaaf Dawoodjee is a Solutions Architect Leader that supports AdTech customers in the Digital Native Business (DNB) segment at HAQM Web Service (AWS). He has over 20 years of experience in Technology and has deep expertise in Analytics. He is passionate about helping drive successful business outcomes for his customers leveraging the AWS cloud.

Scott Bell is a dynamic leader and innovator with 25+ years of technology management experience. He is passionate about leading and developing teams in providing technology to meet the challenges of global users and businesses. He has extensive experience in leading technology teams which provide global technology solutions supporting 35+ languages. He is also passionate about the way the AI and Generative AI transform businesses and the way they support customer’s current unmet needs.

Sachin Shetti is a Principal Customer Solution Manager at AWS. He is passionate about helping enterprises succeed and realize significant benefits from cloud adoption, driving everything from basic migration to large-scale cloud transformation across people, processes, and technology. Prior to joining AWS, Sachin worked as a software developer for over 12 years and held multiple senior leadership positions leading technology delivery and transformation in healthcare, financial services, retail, and insurance. He has an Executive MBA and a Bachelor’s degree in Mechanical Engineering.