Automating product description technology with Amazon Bedrock

In at the moment’s ever-evolving world of ecommerce, the affect of a compelling product description can’t be overstated. It may be the decisive issue that turns a possible customer right into a paying buyer or sends them clicking off to a competitor’s website. The guide creation of those descriptions throughout an unlimited array of merchandise is a labor-intensive course of, and it might decelerate the speed of recent innovation. That is the place Amazon Bedrock with its generative AI capabilities steps in to reshape the sport. On this submit, we dive into how Amazon Bedrock is remodeling the product description technology course of, empowering e-retailers to effectively scale their companies whereas conserving helpful time and assets.

Unlocking the ability of generative AI in retail

Generative AI has captured the eye of boards and CEOs worldwide, prompting them to ask, “How can we leverage generative AI for our enterprise?” One of the crucial promising functions of generative AI in ecommerce is utilizing it to craft product descriptions. Retailers and types have invested vital assets in testing and evaluating the best descriptions, and generative AI excels on this space.

Creating partaking and informative product descriptions for an unlimited catalog is a monumental activity, particularly for world ecommerce platforms. Guide translation and adaptation of product descriptions for every market consumes time and assets. This ends in generic or incomplete descriptions, resulting in lowered gross sales and buyer satisfaction.

The facility of Amazon Bedrock: AI-generated product descriptions

Amazon Bedrock is a completely managed service that simplifies generative AI growth, providing high-performing basis fashions (FMs) from main AI corporations like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon by means of a single API. It offers a complete set of capabilities for constructing generative AI functions whereas making certain privateness and safety are maintained. With Amazon Bedrock, you’ll be able to experiment with numerous FMs and customise them privately utilizing strategies like fine-tuning and Retrieval Augmented Technology (RAG). The platform allows you to create managed brokers for advanced enterprise duties with out the necessity for coding, comparable to reserving journey, processing insurance coverage claims, creating advert campaigns, and managing stock.

For instance, ecommerce platforms can initially generate fundamental product descriptions that embody measurement, coloration, and value. Nonetheless, Amazon Bedrock’s flexibility permits these descriptions to be fine-tuned to include buyer opinions, combine brand-specific language, and spotlight particular product options, leading to tailor-made descriptions that resonate with the audience. Furthermore, Amazon Bedrock gives entry to basis fashions from Amazon and main AI startups by means of an intuitive API, making your entire course of seamless and environment friendly.

Utilizing AI can have the next affect on the product description course of:

  • Quicker approvals – Distributors expertise a streamlined course of, shifting from product itemizing to approval in below an hour, eliminating irritating delays
  • Improved product itemizing velocity – When automated, your ecommerce market sees a surge in product listings, providing shoppers entry to the newest merchandise almost instantaneously
  • Future-proofing – By embracing cutting-edge AI, you safe your place as a forward-looking platform prepared to fulfill evolving market calls for
  • Innovation – This answer liberates groups from mundane duties, permitting them to give attention to higher-value work and fostering a tradition of innovation

Resolution overview

Earlier than we dive into the technical particulars, let’s see the high-level preview of what this answer gives. This answer will help you create and handle product descriptions on your ecommerce platform. It empowers your platform to:

  • Generate descriptions from textual content – With the ability of generative AI, Amazon Bedrock can convert plain textual content descriptions into vivid, informative, and charming product descriptions.
  • Craft photos – Past textual content, it might additionally craft photos that align completely with the product descriptions, enhancing the visible enchantment of your listings.
  • Improve current content material – Do you will have current product descriptions that want a contemporary perspective? Amazon Bedrock can take your present content material and make it much more compelling and interesting.

This answer is obtainable within the AWS Solutions Library. We’ve offered detailed directions within the accompanying README file. The README file incorporates all the data you must get began, from necessities to deployment tips.

The system structure contains a number of core elements:

  • UI portal – That is the person interface (UI) designed for distributors to add product photos.
  • Amazon Rekognition Amazon Rekognition is a picture evaluation service that detects objects, textual content, and labels in photos.
  • Amazon Bedrock – Basis fashions in Amazon Bedrock use the labels detected by Amazon Rekognition to generate product descriptions.
  • AWS Lambda AWS Lambda offers serverless compute for processing.
  • Product database – The central repository shops vendor merchandise, photos, labels, and generated descriptions. This could possibly be any database of your selection. Be aware that on this answer, the entire storage is within the UI.
  • Admin portal – This portal offers oversight of the system and product listings, making certain easy operation. This isn’t a part of the answer; we’ve added it for understanding.

The next diagram illustrates the circulation of knowledge and interactions throughout the system

Image is a picture with white background that has text describing the workflow.The workflow includes the following steps: 1. The client initiates a request to the Amazon API Gateway REST API. 2. Amazon API Gateway passes the request to AWS Lambda through a proxy integration. 3. When operating on product image inputs, AWS Lambda calls Amazon Rekognition to detect objects in the image. 4. AWS Lambda calls LLMs hosted by Amazon Bedrock, such as the Amazon Titan language models, to generate product descriptions. 5. The response is passed back from AWS Lambda to Amazon API Gateway. 6. Finally, HTTP response from Amazon API Gateway is returned to the client.

The workflow contains the next steps:

  1. The shopper initiates a request to the Amazon API Gateway REST API.
  2. Amazon API Gateway passes the request to AWS Lambda by means of a proxy integration.
  3. When working on product picture inputs, AWS Lambda calls Amazon Rekognition to detect objects within the picture.
  4. AWS Lambda calls LLMs hosted by Amazon Bedrock, such because the Amazon Titan language fashions, to generate product descriptions.
  5. The response is handed again from AWS Lambda to Amazon API Gateway.
  6. Lastly, HTTP response from Amazon API Gateway is returned to the shopper.

Instance use case

Think about a vendor uploads a product picture of footwear, and Amazon Rekognition identifies key attributes like “white footwear,” “sneaker,” and “sturdy.” The Amazon Bedrock Titan AI takes this data and generates a product description like, “Here’s a draft product description for a canvas operating shoe primarily based on the product photograph: Introducing the Canvas Runner, the right light-weight sneaker on your energetic life-style. This operating shoe includes a breathable canvas higher with leather-based accents for a trendy, traditional look. The lace-up design offers a safe match, whereas the padded tongue and collar add consolation. Inside, a detachable cushioned insole helps and comforts your toes. The EVA midsole absorbs shock with every step, lowering fatigue. Flex grooves within the rubber outsole guarantee flexibility and traction. With its easy, retro-inspired model, the Canvas Runner seamlessly transitions from exercises to on a regular basis put on. Whether or not you’re operating errands or operating miles, this versatile sneaker will preserve you shifting in consolation and magnificence.”
Image is picture in white background with shoes and tabs in yellow color.

Design particulars

Let’s discover the elements in additional element:

  • Person interface:
    • Entrance finish – The entrance finish of the seller portal permits distributors to add product photos and shows product listings.
    • API calls – The portal communicates with the backend by means of APIs to course of photos and generate descriptions.
  • Amazon Rekognition:
    • Picture evaluation – Triggered by API calls, Amazon Rekognition analyzes photos and detects objects, textual content, and labels.
    • Label output – It outputs label knowledge derived from the evaluation.
  • Amazon Bedrock:
    • NLP textual content technology – Amazon Bedrock makes use of the Amazon Titan pure language processing (NLP) mannequin to generate textual descriptions.
    • Label integration – It takes the labels detected by Amazon Rekognition as enter to generate product descriptions.
    • Type matching – Amazon Bedrock offers fine-tuning capabilities for Amazon Titan fashions to make sure that the generated descriptions match the model of the platform.
  • AWS Lambda:
    • Processing – Lambda handles the API calls to companies.
  • Product database:
    • Versatile database – The product database is chosen primarily based on buyer preferences and necessities. Be aware this isn’t offered as a part of the answer.

Further capabilities

This answer goes past simply producing product descriptions. It gives two extra unbelievable choices:

  • Picture and outline technology from textual content – With the ability of generative AI, Amazon Bedrock can take textual content descriptions and create corresponding photos together with detailed product descriptions. Take into account the potential:
    • Immediately visualizing merchandise from textual content.
    • Automating picture creation for big catalogs.
    • Enhancing buyer expertise with wealthy visuals.
    • Decreasing content material creation time and prices.
  • Description enhancement – If you have already got current product descriptions, Amazon Bedrock can improve them. Merely provide the textual content and the immediate, and Amazon Bedrock will skillfully improve and enrich the content material, rendering it extremely charming and interesting on your clients.


Within the fiercely aggressive world of ecommerce, staying on the forefront of innovation is crucial. Amazon Bedrock gives a transformative functionality for e-retailers trying to improve their product content material, optimize their itemizing course of, and drive gross sales. With the ability of AI-generated product descriptions, companies can create compelling, informative, and culturally related content material that resonates deeply with clients. The way forward for ecommerce has arrived, and it’s pushed by machine studying with Amazon Bedrock.

Are you able to unlock the complete potential of AI-powered product descriptions? Take the following step in revolutionizing your ecommerce platform. Go to the AWS Solutions Library and discover how Amazon Bedrock can rework your product descriptions, streamline your processes, and enhance your gross sales. It’s time to supercharge your ecommerce with Amazon Bedrock!

Concerning the Authors

Dhaval Shah is a Senior Options Architect at AWS, specializing in Machine Studying. With a robust give attention to digital native companies, he empowers clients to leverage AWS and drive their enterprise development. As an ML fanatic, Dhaval is pushed by his ardour for creating impactful options that convey optimistic change. In his leisure time, he indulges in his love for journey and cherishes high quality moments together with his household.

Doug Tiffan is the Head of World Broad Resolution Technique for Vogue & Attire at AWS. In his position, Doug works with Vogue & Attire executives to grasp their targets and align with them on the perfect options. Doug has over 30 years of expertise in retail, holding a number of merchandising and expertise management roles. Doug holds a BBA from Texas A&M College and relies in Houston, Texas.

Nikhil Sharma is a Options Structure Chief at Amazon Net Companies (AWS) the place he and his staff of Options Architects assist AWS clients resolve vital enterprise challenges utilizing AWS cloud applied sciences and companies.

Kevin Bell is a Sr. Options Architect at AWS primarily based in Seattle. He has been constructing issues within the cloud for about 10 years. You could find him on-line as @bellkev on GitHub.

Nipun Chagari is a Principal Options Architect primarily based within the Bay Space, CA. Nipun is keen about serving to clients undertake Serverless expertise to modernize functions and obtain their enterprise targets. His latest focus has been on aiding organizations in adopting fashionable applied sciences to allow digital transformation. Other than work, Nipun finds pleasure in taking part in volleyball, cooking and touring together with his household.

Marshall Bunch is a Options Architect at AWS serving to North American clients design safe, scalable and cost-effective workloads within the cloud. His ardour lies in fixing age-old enterprise issues the place knowledge and the latest applied sciences allow novel options. Past his skilled pursuits, Marshall enjoys mountaineering and tenting in Colorado’s stunning Rocky Mountains.

Altaaf Dawoodjee is a Options Architect Chief that helps AdTech clients within the Digital Native Enterprise (DNB) phase at Amazon Net Service (AWS). He has over 20 years of expertise in Expertise and has deep experience in Analytics. He’s keen about serving to drive profitable enterprise outcomes for his clients leveraging the AWS cloud.

Scott Bell is a dynamic chief and innovator with 25+ years of expertise administration expertise. He’s keen about main and growing groups in offering expertise to fulfill the challenges of world customers and companies. He has in depth expertise in main expertise groups which offer world expertise options supporting 35+ languages. He’s additionally keen about the best way the AI and Generative AI rework companies and the best way they help buyer’s present unmet wants.

Sachin Shetti is a Principal Buyer Resolution Supervisor at AWS. He’s keen about serving to enterprises succeed and understand vital advantages from cloud adoption, driving every little thing from fundamental migration to large-scale cloud transformation throughout folks, processes, and expertise. Previous to becoming a member of AWS, Sachin labored as a software program developer for over 12 years and held a number of senior management positions main expertise supply and transformation in healthcare, monetary companies, retail, and insurance coverage. He has an Government MBA and a Bachelor’s diploma in Mechanical Engineering.

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