CodeScan

1. Challenge

Development teams need faster, more accessible security vulnerability scanning without the overhead of enterprise-only tooling, and the product needed strong SEO, performance, and accessibility to compete for organic traffic.

2. Business Requirements

• Reliable, explainable vulnerability detection using a dual-model AI pipeline.
• Strong technical SEO, performance, and accessibility to support organic growth.
• Stable production deployments free of stale-routing and analytics-tracking gaps.

3. Proposed Architecture

A Next.js application routes scan requests through a dual-provider LLM analysis pipeline, with Supabase for account and scan-result storage and Stripe for subscription billing.

4. Implementation Approach

Delivered a focused SEO/performance/accessibility overhaul as a reviewed and merged change set, then diagnosed and fixed a production routing bug caused by a stale deployment reference and a gap in analytics event collection.

5. Security Considerations

As a security-tooling product, scan results and account data are isolated per authenticated user, and production issues (stale routing, missing analytics) were treated as reliability defects and fixed promptly.

6. Results

Delivered a merged SEO/performance/accessibility overhaul and resolved two distinct production reliability bugs post-launch.

7. Technologies Used

• Next.js
• React
• TypeScript
• Supabase
• Stripe
• Anthropic Claude
• OpenAI