AI Software Factory | AI Code Generation Platform
AngenAI: The AI Code Generation Platform for Modern Teams
AngenAI is an AI code generation platform built for companies and individual developers. Turn your entire software development process into a structured, AI-powered production pipeline that enforces your coding standards automatically.
How Our AI Code Generation Platform Works
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, including 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 and subsequently 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—especially valuable when teams scale beyond a few people.
Development Tool Integration
The 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, reads relevant code from GitHub repositories, and references company guidelines stored in Confluence. This integration eliminates manual copying of context between tools, reducing 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, keeping code private and working 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 and handles large codebases more efficiently than browser-based alternatives. Learn more about getting started with AngenAI or explore our pricing plans.
AI Code Generation Platform for Consistent Quality
The platform enforces the same coding standards across all contributors—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 by preventing 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, automated quality assurance testing, and release process automation.
These additions will cover more of the software development lifecycle, reflecting our focus on practical development solutions.
Why Choose This AI Code Generation Platform
AngenAI solves a specific problem we encountered in our own work: maintaining code quality and consistency as projects grow and teams expand.
The platform reduces manual coding time significantly while enforcing standards that would otherwise require extensive code reviews. This combination makes development more efficient and reliable.
