Artificial intelligence is becoming a powerful tool for web development, automating error detection and speeding up the testing process. In this article, we'll explore how to apply AI at various stages of website development—from code review to accessibility and SEO analysis. What exactly are we looking for with AI? First, code bugs: syntax errors, invalid dependencies, and anti-patterns. Second, performance: slow load times, rendering bottlenecks, and unoptimized resources. Third, accessibility: missing image captions, contrast, and navigation for screen readers. Finally, SEO vulnerabilities: slow server responses, duplicate pages, and canonical issues. What tools to use 1) Linters and static code analysis: Tools like ESLint, flake8, and SonarQube help detect errors before launch. 2) Automated tests: Unit and integration tests supported by AI analytics to detect regressions. 3) Performance monitoring: APM solutions and Lighthouse/WebPageTest audits with conclusions supplemented with AI recommendations. 4) Accessibility analysis: Audit tools (AXE, WAVE) paired with AI models to prioritize fixes. 5) Finding broken links and structural issues: Crowdflow checks and AI-powered scripts to identify inconsistencies. How to act in practice Start with a code audit: enable a static analyzer, run automated tests, and configure CI for regular checks. Then, launch a performance crowd-reporting exercise: record baseline metrics and use AI to analyze the causes of delays. The next step is an accessibility audit: identify elements without alternative text and poor contrast. Include SEO checks in your daily pipeline: check for response speed, redirects, and duplicate content. Examples of practical scenarios: AI can suggest specific CSS edits, suggest JavaScript function replacements, recommend optimal headers and canonical URLs, and so on. It's important to maintain a balance between automation and manual verification to avoid missing context or UX details. Bottom line: Regular AI-powered audits help reduce error rates, speed up releases, and improve website quality. Integrate tools into your workflow and keep your team informed of progress through clear dashboards and reports. The team's next steps are to identify a set of priority metrics, set up automatic notifications for critical issues, and regularly update AI check rules based on changes on the site.