Building Enterprise Ext JS Applications Instantly with Indi Engine AI

Pavel Perminov, the solo founder and developer of Indi Engine AI, for an insightful session at JS Days 2025. Pavel showcased his unique “zero code” tool designed for building data-intensive Ext JS applications. This innovative platform leverages AI prompts and offers advanced features like real-time multi-window updates, robust GitHub-based backups, and simplified deployment.

Here is a look at the key concepts and functionality demonstrated during Pavel’s presentation, “Building Real-Time Multi-Window Ext JS Apps with AI Prompts”.

Generative AI for Application Development

Indi Engine AI represents a true form of generative AI tailored for business use cases. It allows developers to describe the required application using natural language prompts.

Pavel demonstrated how simply asking the AI model for an app—for example, “I need an app for the zoo management” or “I need an app for a real estate company”—results in the immediate generation of a working application. The AI model designs and generates all the relevant components:

• Entities and fields.
• Relationships (including real foreign key constraints in the database).
• Data views, user roles, and permissions.
• Sample data to explore the functional app immediately.

This process, when using the 2.0 zero model, typically takes about 30 to 50 seconds to respond, delivering an explorative application in a matter of minutes.

Real-Time, Multi-Window UI Updates

One of the standout features of Indi Engine is its capability for real-time updates across the entire multi-window UI. If a user opens multiple records, grids, or views, and one of those records is modified by another user or a script, the changes are reflected immediately in the UI.

This sophisticated real-time system is implemented using several components:

1. MySQL binary log: This log is read by a special Java process.
2. Rabbit MQ: The changes are pushed to Rabbit MQ.
3. PHP process: A PHP process reads those changes, converts them into a special JSON format, and pushes them to dedicated Rabbit MQs responsible for each opened user browser tab.
4. Independent Updates: This ensures that each browser tab receives updates independently.

Effortless Deployment and Maintenance

Indi Engine AI is designed for developer convenience, making deployment and maintenance straightforward.

Simple Deployment via Docker

The system runs as a Docker Compose project. Deployment requires cloning the repository and running an installation script, which typically gets an instance up and running in several minutes. The project utilizes four containers: MySQL, RabbitMQ, an Apache container, and a wrapper container.

One-Click Updates

Pavel emphasized the ease of keeping the system updated. Developers can simply click the update button in the UI. Indi Engine automatically handles everything else, including:

• Checking if packages or the local clone are outdated.
• Pulling fresh changes.
• Running migrations.
• Rebuilding images (if Docker files were changed) and restarting containers.

Free, Robust Backups on GitHub

The backup system is based on GitHub releases, allowing for powerful and free storage options. Every backup created in Indi Engine is shown as a release in the corresponding GitHub repository. GitHub permits uploading up to 1,000 assets to each release, meaning each backup can be up to 2 terabytes in size.

Indi Engine supports custom backups and automatically creates periodical backups (e.g., seven daily, five weekly, and twelve monthly backups).

Advanced Features: Localization and Scaling

Seamless Localization

The presentation included a demonstration of how the application’s UI can be quickly translated. This feature uses the Google Cloud Translation API to process titles and labels. Translations of UI elements (such as column headings, field labels, tabs, and buttons) are stored in the database as JSON objects, with the language codes serving as the keys (e.g., es for Spanish). Pavel showcased successful translations into Spanish and Chinese.

Handling Large Datasets

While still a young product, Indi Engine is built with scalability in mind. Pavel noted that he has had experience with databases containing 13 million records, resulting in a volume size of 21 gigabytes, which could be exported in seven minutes.

For future real enterprise-level scaling, Pavel plans to integrate Vitess, the database horizontal scaling engine used by YouTube. Once integrated, this will enable the handling of tens of billions of records.

Pavel noted that detailed documentation, currently spanning 137 pages, is in development, with future improvements planned for AI prompt attachment support to yield more precise designs.

See the Revolution in Action

To see the incredible demonstration of how Indi Engine AI builds complex Ext JS applications instantly—including the impressive showcase of localization into Chinese and the detailed look at simple deployment and GitHub-based backups—watch the full session replay on the JS Days 2025 page. The presentation, “JS Days 2025 – Indi Engine AI: Building Real-Time Multi-Window Ext JS Apps with AI Prompts,” features Pavel Perminov, the solo founder and developer of Indi Engine AI, giving you all the information directly from the source.
You can also learn more about the product by visiting the official website, Indi-engine.ai

Disclaimer: The content presented here has been created by a third-party provider. It is not affiliated with or endorsed by Sencha. The material is intended solely to demonstrate potential use cases and explore the capabilities of the Sencha framework.