SimpleOne launches SimpleGen, an AI tool for building and deploying on its platform

SimpleOne has released SimpleGen, an AI tool that helps generate solutions for the SimpleOne platform directly from the project repository. Alongside the release, the company showed a basic launch scenario: from installing the CLI to the first task and deployment to an instance.

What SimpleOne showed

SimpleGen is a new layer of AI automation around the SimpleOne platform. The tool is installed on a working machine via npm, connects to a platform instance by URL and uses existing credentials or an API key. Nothing needs to be installed on the instance itself: the logic works through repository access and a link to the environment.

In essence, the company is offering not a standalone chatbot for code generation but a managed workflow tied to the project structure, Git and development scenarios inside SimpleOne.

In its published guide, SimpleOne emphasizes not the abstract capabilities of AI but a reproducible process. First, the developer initializes the project, pulls the application data from the instance and records the starting state in Git. After that, SimpleGen can be used as a working tool for setting tasks, generating changes and then building.

This approach matters for enterprise development: you can see exactly what the AI created, what changed after each iteration and which state you can quickly roll back to if the generation produced the wrong result.

What you need to get started

The barrier to entry looks fairly down-to-earth: you need access to a SimpleOne instance, a local repository and a basic set of development tools. The company stresses that almost any instance will do — dev, test, stage or a personal environment — and hosting can be either in the cloud or on an internal network. The main condition is that the instance must be reachable over the network and the developer must have working credentials.

• SimpleOne instance URL
• API key, or an administrator login and password
• Node.js, npm and Git on the working machine
• The Cursor editor for running commands via chat

Setup then continues through environment variables: the `.env` file specifies the instance address and the authorization method. If the project is empty, SimpleGen initializes it with the init command and exports the application records along with the platform's required dependencies.

After the first export, SimpleOne advises making a starting commit right away. This is not a formality: a baseline commit makes it easier to compare changes, helps with safe rollbacks and makes teamwork more predictable if several developers or reviewers go on to work on the same product.

What the workflow looks like

The main scenario runs in Cursor, where SimpleGen commands are launched as slash commands inside the chat. The user first creates a task specification, then asks the system to break it into a plan, and then triggers generation of changes based on the prepared structure. In practice, this means going from a human description of a feature to files that can already be checked in a diff, refined and accepted.

SimpleOne makes a point of showing that AI does not replace engineering discipline here: after every step, review, commit and a deliberate acceptance of changes still remain.

"Commands are launched in the

Cursor chat via the / menu, not as ordinary text."

As an example, the company walks through a simple task: making the urgency field mandatory when saving a task. The developer writes a title, a description and acceptance criteria, starts spec creation, gets a plan in task.md, then calls code and configuration generation. After the changes are reviewed, the result is committed and sent to build, followed by deployment to a SimpleOne instance.

One important detail: the guide immediately describes typical failures, from Git missing from PATH to fetch errors and Cursor connection problems. In other words, the product is presented not as magic but as a tool with clear operating conditions.

What this means

SimpleOne is moving toward more structured AI development for its platform: not just "generate me some code" but a full cycle from specification to deployment. For teams that already live in Git and work with instances, this may noticeably cut the time spent on routine changes and lower the barrier to entering the platform.

If the company delivers integrations such as Figma and extends the scenarios to complex tasks, SimpleGen could grow from a starter assistant into a permanent tool for product development.