Annote.it — Automated AI Copyeditor

The Inspiration

During my time working with student media at Cal State Long Beach, I noticed that many student journalists were juggling demanding workloads and tight deadlines, often leading to avoidable mistakes. These errors weakened publication integrity and reduced reader trust.

Annote.it was designed to solve that problem by providing journalists with a smart, accessible tool that understands their unique style guide, verifies facts, and enhances SEO performance automatically.

Process

The project evolved over three months and combined editorial research with AI-assisted automation and web development.

• Design: Built a minimal interface focused on clarity and feedback visibility.
• Development: Integrated Python-based grammar and style checks, Firebase for hosting, and real-time authentication.
• Testing: Beta-tested using real article data and refined rules through feedback loops.

System Architecture

Each submitted article triggers a backend API call that processes grammar, SEO, and fact-checking through the Python engine before returning annotated suggestions directly to the web interface.

• Frontend: HTML, CSS, JavaScript for user interface
• Backend: Firebase for hosting, authentication, and database management
• Processing Engine: Python NLP modules for grammar, tone, and style rules
• APIs: Grammar correction, SEO keyword scoring, and fact validation

Key Takeaways

This project showed me how AI can enhance journalistic workflows by freeing editors to focus on storytelling instead of mechanics.

• Customization by publication style guide was essential for adoption.
• Blending UX design and automation improved editor trust in AI output.
• Iterative feedback loops with real editors shaped both technical and editorial design decisions.

Project Gallery

Snapshots from the Annote.it interface and prototype testing phase.

Suggestion Feature

Sharing Feature