Pragatix trains a private AI model on your existing repositories—functions, classes, documentation, and patterns—so it can deliver context-aware answers, generate code that matches your standards, and provide accurate autocomplete suggestions aligned with your architecture.

Yes. Security is one of the core pillars.
Pragatix runs on a private, secure AI environment, meaning your code never leaves your ecosystem.
No code is shared, stored externally, or used to train public models.

Absolutely. The assistant supports multi-language environments, providing autocomplete, code search, and generation whether you’re working in JavaScript, Python, PHP, Java, C#, Go, Ruby, or others.

Developers can ask natural-language questions such as:

• “Where is the login function defined?”
• “Which service handles invoice generation?”
• “Show me how we validate JWT tokens.”
  
  

The assistant parses your codebase and provides accurate, context-rich answers instantly—no more digging through files manually.

It can do both. The assistant can generate:

• boilerplate code
• reusable components
• API integrations
• full functions
• troubleshooting or refactoring suggestions

Simply provide a prompt, and it writes code consistent with your existing style and patterns.

Yes. The assistant can identify potential issues and suggest corrections, optimized code, or best-practice alternatives. Developers can ask:
“Why is this function failing?” or “Optimize this SQL query,” and receive actionable fixes.

Yes. Pragatix integrates seamlessly with popular IDEs such as VS Code, JetBrains environments, and other developer tools. It enhances your workflow without requiring major changes to the way your team works.

Definitely. The Code Documentation feature can automatically generate comments, descriptions, summaries, and explanations for functions or modules—making future maintenance easier and improving onboarding for new developers.

By automating repetitive tasks, reducing time spent searching through the codebase, and accelerating development cycles. Teams ship faster, encounter fewer errors, and maintain greater consistency across codebases.