Article Factory

Generates full-length blog articles by combining pre-built content templates with LLM-driven paragraph expansion and keyword placement. The system accepts a topic, target keywords, and article length, then uses prompt chaining to generate introduction, body sections, and conclusion while attempting to naturally incorporate SEO terms. This approach prioritizes speed over originality, relying on template scaffolding rather than deep research or fact verification.

Generates images from text prompts using an integrated diffusion model (likely Stable Diffusion or similar) with pre-configured style templates (e.g., 'stock photo', 'illustration', 'infographic'). Users input a description and select a style; the system applies template-specific negative prompts and parameter adjustments to guide generation. Output images are typically 512x512 or 1024x1024 resolution with minimal customization of aspect ratio or advanced parameters.

Accepts a CSV or JSON file containing multiple article topics, keywords, and metadata, then queues them for parallel generation and optional scheduled publishing. The system processes batches asynchronously, storing generated content in a dashboard for review and export. Users can set publication dates and integrate with WordPress or other CMS platforms via API or webhook for automated posting.

Allows users to select from pre-defined voice profiles (e.g., 'professional', 'casual', 'humorous', 'technical') that adjust the LLM's system prompt and generation parameters. The system applies tone-specific vocabulary, sentence structure, and phrasing patterns to generated content. However, customization is limited to selecting from a fixed set of profiles rather than training custom models or fine-tuning on brand-specific examples.

Generates multi-level article outlines (H1, H2, H3 headings with bullet points) from a topic and target keyword. The system uses prompt chaining to create a logical content structure, then allows users to expand individual sections into full paragraphs. Outlines are presented in an interactive editor where users can reorder sections, add custom headings, or delete irrelevant content before triggering full article generation.

Generates articles in multiple languages (typically 20-50 supported languages) by translating English prompts and content through an integrated translation API or multilingual LLM. The system applies language-specific formatting (e.g., date formats, number separators) and attempts basic cultural adaptation. However, localization is primarily translation-based rather than culturally-aware rewriting.

Tracks metrics for generated articles (views, engagement, time-on-page, bounce rate) when integrated with Google Analytics or CMS platforms, then recommends content improvements or topic variations based on performance data. The system uses simple heuristics (e.g., 'high bounce rate suggests weak introduction') and may suggest regenerating sections with different tones or keywords.

Scans generated articles against a database of web content and other generated articles to detect plagiarism or excessive similarity. The system returns an originality score (typically 0-100%) and highlights sections that match existing content. This is implemented via API calls to plagiarism detection services (e.g., Copyscape, Turnitin) or custom similarity matching using embeddings.

Automatically generates SEO metadata (meta title, meta description, slug, alt text for images) optimized for target keywords and search intent. The system uses keyword analysis and SERP data (if available) to craft titles and descriptions that balance keyword inclusion with click-through rate optimization. Generated metadata follows best practices (title length ~60 characters, description ~160 characters) and can be customized before publishing.

Provides a library of pre-built content templates (e.g., 'How-to guide', 'Product review', 'List article', 'Case study') that scaffold article structure and prompt engineering. Users can select a template, fill in topic-specific variables, and generate content that follows the template's structure. Advanced users can create custom templates by defining variable placeholders and LLM instructions, then save them for reuse across the team.