Conch

Generates essay drafts and sections using language models with context awareness of user-provided prompts, research materials, and writing style preferences. The system maintains coherence across multi-paragraph outputs by tracking essay structure and previously generated content, enabling iterative refinement of thesis statements, body paragraphs, and conclusions without losing thematic continuity.

Scans generated and user-written content against a database of published academic works, web sources, and previously submitted essays using fingerprinting and semantic similarity matching. Returns an originality score (0-100%) with highlighted sections flagged as potential matches, enabling writers to identify and revise problematic passages before submission to institutions.

Ingests research materials (PDFs, web articles, academic papers) and extracts key citations, quotes, and source metadata (author, publication date, DOI) into a structured format. Automatically generates in-text citations and bibliography entries in multiple citation styles (APA, MLA, Chicago) and embeds source references directly into generated essay content, reducing manual citation formatting work.

Generates essays in multiple academic and professional writing styles (formal academic, persuasive, analytical, narrative) with configurable tone parameters (formal/casual, confident/cautious, technical/accessible). Uses style-specific prompting and post-generation filtering to ensure output matches the requested voice, enabling users to generate multiple versions of the same content for different audiences or assignment requirements.

Analyzes generated or user-written essay sections and provides targeted revision suggestions for clarity, argumentation strength, evidence support, and academic tone. Uses rubric-aware evaluation (if user provides assignment rubric) to prioritize suggestions that address specific grading criteria. Enables one-click acceptance of suggestions with automatic content replacement, supporting iterative improvement without manual rewriting.

Generates detailed essay outlines from a topic, thesis statement, or assignment prompt, with hierarchical structure (main sections, subsections, key points). Outlines include suggested argument flow, counterargument placement, and evidence allocation. Users can edit the outline before generation, ensuring the essay follows their intended structure rather than AI-determined organization.

Generates plagiarism reports in formats compatible with institutional submission systems (Turnitin, Canvas, Blackboard) and provides transparency about AI-generated vs. human-written content. Reports include metadata about which sections were AI-generated, enabling institutions to apply appropriate policies for AI-assisted work. Supports institutional compliance workflows where educators need to verify both originality and AI usage.

Provides real-time feedback on grammar, syntax, clarity, and academic tone as users write or paste content. Uses NLP-based error detection to identify issues (subject-verb agreement, comma splices, passive voice overuse) with explanations and suggested corrections. Integrates with the essay editor to highlight errors inline and offer one-click fixes without disrupting the writing flow.