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As a business looking to rapidly expand our digital presence, we approached Dynamic Code with a need to build a personalized e-commerce app that could cater to multiple customer segments. The catch? We had limited product data, and we needed it done in just two weeks. Thanks to Dynamic Code integration of generative AI technologies, this ambitious project became a reality.
Wolfgang Riegler
Imagine launching a fully functional e-commerce app from scratch, without any pre-existing products, data, or content—and completing it in just two weeks. That’s exactly what the team at Dynamic Code did using generative AI, showcasing the strength of different MACH components (Microservices, API-first, Cloud-native, and Headless) and how they can be orchestrated into a unified system.
Step 1: Concept and Brainstorming
The project began with a brainstorming session, where the team conceived a fictional B2B company, MACHsupply, that sells construction tools to various professional audiences. With this concept in place, ChatGPT was used to generate the background and define the scope for the demo app.
Step 2: Products and Target Audience
The team turned to ChatGPT to examine competitors like Ace Hardware and True Value creating relevant product categories for construction retail. It also developed customer personas, mapping each persona to specific product categories, ensuring a personalized buying experience.
Step 3: Generating Product Data
To populate the app, ChatGPT helped create a complete Product Information Management (PIM) system. This included 100 fictional products, each with detailed descriptions such as product IDs, dimensions, and technical specs. A CSV file of product data was generated through a PHP script written by ChatGPT’s Advanced Data Analytics.
Step 4: Product Imagery
DALL·E was employed to generate high-quality images for the product catalog. Using prompts designed by ChatGPT, the team was able to generate images for all 100 products quickly. A PHP script automated the export of these images with the appropriate product IDs.
Step 5: Visual and Content Creation
To add a creative touch, MidJourney was used to create logos, homepage visuals, and persona-specific imagery for blog posts. The CMS system allowed for generative AI to rewrite generic content into highly personalized articles tailored to different target audiences.
Step 6: AI-Powered Chat Functionality
The team integrated LangChain and ChatGPT to create an intelligent chat function, which allowed customers to interact with the app. Users could ask for product recommendations, compare items, and even manage their shopping cart through the chatbot—fully connected to the commercetools platform and the CMS system, Kontent.ai.
Result: A Functional and Personalized E-Commerce App
In just two weeks, the team built a fully functional Flutter app, offering a vast range of products with a personalized experience tailored to various construction professionals. Whether users were purchasing in bulk or seeking specialized items, the app catered to their needs efficiently.
Takeaway
The project underscored the power of generative AI when combined with MACH architecture. By leveraging customer data platforms like Segment, e-commerce platforms like commercetools, and headless CMS systems like kontent.ai, businesses can create hyper-personalized, scalable, and efficient digital experiences in record time.
In this case, the blend of AI tools—ChatGPT, DALL·E, MidJourney, LangChain, and FlowiseAI—enabled the creation of an innovative and personalized app, demonstrating the future of AI-driven e-commerce solutions.
This rapid development approach not only enhances user experience but also gives companies a competitive edge in a fast-evolving digital landscape.
