Find the problem.
Understand the cause.
We do not treat AI as autonomous magic. ScanFix pairs deterministic browser evidence with cascading language models to help engineers understand why websites fail and how to fix them.
Evidence first. Reasoning second.
ScanFix strictly grounds AI reasoning in concrete DOM selectors and HTTP response metrics to prevent hallucinations.
Deterministic Detection
The scanner identifies a concrete defect through rule-based evaluation (e.g., LCP degradation, missing alt attribute, CSP header absence).
Telemetry Capture
ScanFix extracts the exact DOM selector, HTML snippet, network waterfall timings, and request headers surrounding the failure.
AI Engineering Analysis
The cascading AI model synthesizes the evidence against modern frontend frameworks, distinguishing primary causes from downstream symptoms.
Actionable Code Diff
Produces concrete, framework-specific code diffs and configuration updates that developers can review, test, and adopt.
Mathematical Re-Audit
Re-evaluates the modified endpoint to confirm the regression is resolved and preserve the established quality baseline.
Engineered with security by default.
Prompt Injection Neutralization
Untrusted website content and scraped HTML are enclosed within strict XML boundaries to prevent malicious injection payloads.
Automated Credential Scrubbing
API keys, bearer tokens, passwords, and private hashes detected in request headers are scrubbed before payload transmission.
Deterministic Cascading Fallback
Multi-provider fallback pipeline across OpenAI, Groq, and Gemini ensures high availability during provider outages.
Test AI investigation with 2 free audits.
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