AI Analysis & Diagnosis
Understand the "Why" behind every crash. DebugX uses proprietary LLMs to analyze error context and provide actionable fixes.
How it works
Context Capture
We gather stack traces, breadcrumbs, network logs, and visual state.
Pattern Logic
Our engine correlates this data with known library patterns and documentation.
LLM Processing
Data is synthesized through our fine-tuned AI to generate a root cause report.
The Analysis Report
01. Root Cause Diagnosis
A concise explanation of the failure. Instead of "Undefined is not a function", you'll see "The `user.profile` object was null because the `/api/user` endpoint returned an empty array for this request."
02. Suggested Code Fix
A code snippet ready to be copied into your project to handle the edge case or fix the logical flaw.
const projects = userData.projects?.map(p => p.id) || [];
03. Developer Prompt
A pre-written prompt you can send to your own AI tools (like Copilot or Cursor) to refactor the entire failing module.
Why AI Debugging?
Traditional monitoring only tells you when something broke. DebugX AI tells you how to fix it before you even open your IDE.
Reduces MTTR (Mean Time To Resolution) by 70%
Eliminates 'Impossible to Reproduce' bugs
Onboards junior engineers faster by explaining complex failures
Prevents regression by identifying similar risk patterns