AngenAI
AngenAI is an AI-assisted software development platform built for companies and individual developers. Turn the entire process of building software into a structured, AI-powered production pipeline.
Automated Code Generation
The AI code generation platform uses ChatGPT, Claude, and DeepSeek models. These models write code based on task descriptions you provide.
First, the system analyzes existing code repositories. Then, it reviews documentation and company guidelines. After that, AngenAI produces code that matches patterns already present in your project. As a result, developers spend less time writing repetitive code structures.
The platform generates several code elements. These include function implementations, API endpoints, database queries, and UI components. All outputs are based on the task requirements you provide.
Company Standard Enforcement
Our AI code generation platform applies specific coding standards for each organization. AngenAI reads the rules defined in company documentation. Subsequently, it applies them to all generated code.
This ensures new code follows the same conventions as existing code. For example, it maintains consistent naming conventions, file structures, and architectural patterns. Therefore, the platform helps maintain consistency when multiple developers work together. This becomes especially valuable when teams scale beyond a few people.
Development Tool Integration
The AI code generation platform connects with your existing tools. Specifically, it integrates with Jira for task management. Additionally, it works with GitHub for version control. Finally, it connects to Confluence for documentation.
AngenAI pulls task descriptions from Jira automatically. Then, it reads relevant code from GitHub repositories. Meanwhile, it references company guidelines stored in Confluence. This integration eliminates manual copying of context between tools. Consequently, you reduce setup time for each coding task.
Desktop Application Design
We built AngenAI as a desktop application rather than a web service. The application runs locally on developer machines. This approach keeps code private and works without internet dependency for core functions.
The workflow includes four simple steps:
- Connect to existing tools during initial setup
- Create work items that gather relevant context
- Send compiled information to AI models for code generation
- Review output before merging into your codebase
The desktop format provides better performance. It handles large codebases more efficiently than browser-based alternatives.
Code Consistency Management
The AI code generation platform enforces the same coding standards across all contributors. It doesn’t matter whether code comes from junior developers, senior engineers, or AI generation. AngenAI checks for matching patterns throughout.
These patterns include variable naming, function structure, error handling, and documentation style. This addresses a common problem in development teams. Specifically, it prevents code quality from varying significantly across team members with different experience levels.
Technical Implementation and Roadmap
Our team developed this platform using modern technologies. We use Electron for the desktop interface. Python handles backend processing. Additionally, we maintain API integrations with multiple AI providers.
The current version handles code generation from task descriptions effectively. However, we have ambitious expansion plans. The roadmap includes requirements drafting from client meetings. Furthermore, it will cover automated quality assurance testing. Finally, we’re adding release process automation.
These additions will cover more of the software development lifecycle. They reflect our focus on practical development solutions.
Why AngenAI Matters
AngenAI solves a specific problem we encountered in our own work. That problem was maintaining code quality and consistency as projects grow. It becomes even more challenging as teams expand.
The AI code generation platform reduces manual coding time significantly. Meanwhile, it enforces standards that would otherwise require extensive code reviews. This combination makes development more efficient and reliable.
