Why we built ScanFix.
Website auditing was broken. Teams were forced to stitch together separate Lighthouse runs, keyword checkers, accessibility checklists, and manual penetration tests.
The Problem: Fragmented, Superficial Auditing
Most auditing tools dump dozens of disconnected warnings without providing code context. Developers spend hours reverse-engineering which CSS selector or third-party script caused an LCP spike. Agencies struggle to communicate value to non-technical stakeholders.
ScanFix was architected to bridge this gap: capturing real headless browser telemetry, synthesizing root causes with evidence-grounded AI models, and automating continuous monitoring to prevent quality regressions.
Our Core Principles
Deterministic Over Vague
Quality is a mathematical discipline. We measure real DOM nodes, exact HTTP headers, and precise network waterfalls instead of generating hand-waving scorecards.
Evidence-Grounded Intelligence
We never treat AI as autonomous magic. Every AI suggestion is strictly grounded in raw browser evidence, CSS selectors, and framework context.
Built for Real Engineering Workflows
Fixing websites requires code diffs, CLI terminals, and pull requests. We build for the engineers and agencies who do the actual work.
Continuous Over Episodic
A website changes the moment you stop looking. Quality audits should be automated, baseline-protected, and continuously monitored.