- The new platform allows product teams to generate editable user interfaces and code through natural language prompts.
- The release follows Tencent's recent move to open-source its Agent Memory tool, aiming to optimize AI development costs.
Tencent has launched a new artificial intelligence (AI) tool that can directly convert text descriptions into editable design drafts and software code.
With just a single sentence, users can quickly generate various design contents such as app pages, official websites, posters, illustrations, and presentations, with flexible and convenient editing operations. This marks the tech giant's latest move to streamline the software development process.
The platform, named Ardot, officially began its public beta testing on Monday. It allows product and design teams to use natural language prompts to generate application interfaces, websites, and presentations, according to an announcement.
Unlike traditional AI image generators, Ardot produces editable and reusable design assets.
The platform also supports the direct import of existing Figma files, fully retaining the original layouts and business components during the migration process, the announcement said.
A key feature of the platform is its ability to directly convert design files into code.
By utilizing the Model Context Protocol, Ardot achieves seamless integration with various development tools. These compatible integrated development environments include Cursor and Claude Code.
The platform also provides enterprise users with real-time collaboration and design asset management features. It supports multi-user online real-time commenting, version comparison, and intelligent permission allocation.
In addition, a companion WeChat mini-program will soon be launched to support mobile access, according to the announcement.
The release of the new tool follows Tencent's recent efforts to optimize its AI infrastructure.
The company announced the open-sourcing of its Agent Memory tool last Thursday. The memory tool utilizes core technologies such as context offloading and task canvas, which can reduce AI token consumption by up to 61%.
