Project Background

The client's initial problem was the time-consuming and error-prone process of turning design mockups into HTML. The process required frequent communication between designers and developers, leading to inefficiencies. The main objectives of the project were to automate 80% of the newsletter creation tasks, reduce newsletter production time by 50%, and maintain 90% accuracy in automated processes.

Challenges

The client faced the challenge of a time-consuming manual process of turning design mockups into HTML, which required frequent communication between designers and developers. This process was not only inefficient but also prone to errors.

Solution

To address the client's needs, Agency AI developed an AI agent that generates HTML directly from image files like Figma or Photoshop designs. The agent was built using the Agency Swarm framework, which allows for iteration on sections based on user feedback. The solution involves an AI agent that analyzes input images and design mockups, breaks them into parts, and generates HTML code for each section. It uses computer vision and iterative refinement. A Gradio interface was created for user interaction, which was customized to show the generated HTML in real-time.

Key Features and Functionality

An AI agent was developed that can generate HTML from design mockups, break designs into sections, and allow for interactive refinement through natural language commands. The solution was fine-tuned on the client's data to provide accurate results.

Results and Impact

The project is still ongoing, and the results will be measured based on the reduction in newsletter production time, the percentage of tasks automated, and the accuracy of the automated processes.

Lessons Learned

One of the key lessons learned from this project is the importance of user feedback and iterative development. Allowing users to provide feedback and generate content by sections significantly improved the results and effectiveness of the solution. This approach not only enhanced the accuracy of the generated HTML but also increased user satisfaction by providing a more tailored solution. This lesson will be invaluable for future projects, emphasizing the need for user-centric design and iterative development in AI solutions.

Conclusion

Agency AI developed an AI agent that transforms HTML newsletter building by combining advanced AI, computer vision, and iterative refinement. It automates a significant portion of the HTML generation process, reducing production time and maintaining high accuracy.