Salai: Ghostwriter

This project introduces a next-generation content review and editing platform, moving beyond traditional text comparison to offer a truly intelligent analysis. It leverages advanced AI to understand and compare documents based on their semantic meaning, providing a more intuitive and powerful workflow for collaborators.

Core Innovations
Semantic-First Analysis: At its core, the application features a sophisticated semantic diffing engine. Instead of simple line-by-line comparisons, it uses Natural Language Processing (NLP) models to comprehend the underlying meaning and context of the text. This allows it to intelligently identify paraphrasing, reordered paragraphs, and nuanced edits that conventional tools would miss.

Model-Agnostic AI Integration: The platform is engineered with a flexible, pluggable architecture for its generative AI capabilities. This design allows for seamless integration with a variety of leading LLMs, both cloud-based and local, ensuring the system remains at the cutting edge by adapting to the rapidly evolving AI landscape.

Context-Aware Suggestions: By understanding the document's context, the integrated AI can provide highly relevant suggestions for rewriting, expanding, or summarizing text. It's guided by a sophisticated prompting framework that ensures all AI-generated content aligns with the user's intent and the document's original tone.

Advanced Architecture & Design
Asynchronous Processing for a Fluid UX: The system is built on an asynchronous, multi-threaded architecture. All computationally intensive tasks, especially calls to external AI services, are handled in the background. This ensures the user interface remains responsive and fluid, providing a seamless and professional-grade user experience.

Scalable, Service-Oriented Design: The backend is logically separated into distinct services responsible for AI connectivity, text processing, and state management. This clean, modular design enhances maintainability and scalability, mirroring the principles of a microservices-inspired architecture.

Robust State Management & Edit History: User actions within the review process are managed through a robust system inspired by the Command and Memento design patterns. This creates a reliable and complete history of all review decisions, enabling powerful features like multi-level undo/redo and ensuring data integrity throughout the editing session.

Intuitive User Experience
Interactive Multi-Level Review: The interface empowers users with granular control over the review process. It features a unique, multi-level zoom capability, allowing reviewers to seamlessly switch between high-level paragraph overviews, sentence-level comparisons, and detailed word-by-word analysis.

Clear & Actionable Visuals: The UI provides a clear and intuitive visualization of all proposed changes. Color-coded highlights for insertions, deletions, and replacements allow users to understand the edits at a glance and take immediate action, streamlining the entire review workflow.

Note: This project is a work-in-progress currently in private alpha release for select users.