Cabina AI

Routes text generation requests across multiple LLM providers (OpenAI, Anthropic, Google, etc.) using a decision engine that selects the optimal model based on task type, quality requirements, and cost constraints. The routing layer abstracts provider-specific APIs and prompt formatting, allowing users to specify intent rather than model selection. This approach reduces vendor lock-in and enables cost optimization by matching lightweight tasks to cheaper models while reserving expensive models for complex reasoning.

Provides a single dashboard interface for generating different types of written content (blog posts, social media captions, product descriptions, emails, technical documentation) with task-specific prompt templates and output formatting. The platform pre-configures optimal parameters (temperature, max tokens, system prompts) for each content type, reducing the need for manual prompt engineering. Users can customize templates or create new ones, and the system maintains a library of successful prompts for reuse across projects.

Analyzes generated content for quality metrics including readability (Flesch-Kincaid grade level), sentiment, tone consistency, keyword density, and plagiarism detection. The platform compares generated content against user-defined quality standards and flags content that doesn't meet thresholds. Performance metrics track which templates, models, and prompts produce the highest-quality outputs based on user ratings and objective metrics. Users can export quality reports for review and optimization.

Abstracts image generation across multiple third-party providers (DALL-E, Midjourney, Stable Diffusion, etc.) through a unified API and interface. Users submit text prompts and specify parameters (style, aspect ratio, quality level) without needing to understand provider-specific syntax or limitations. The platform handles prompt translation, parameter mapping, and response normalization across different providers, allowing users to generate images from multiple services without managing separate accounts or APIs.

Integrates text and image generation into a single workflow, allowing users to generate written content and corresponding visuals without switching between tools. For example, users can generate a blog post and then automatically generate featured images, social media graphics, and thumbnail variations from the same content. The platform maintains context between text and image generation, enabling image prompts to be derived from or reference the generated text.

Enables bulk generation of content by importing structured data (CSV or JSON files) containing variables for templates. Users define a template once with placeholders (e.g., {{product_name}}, {{target_audience}}), then upload a file with hundreds or thousands of rows. The platform generates unique content for each row by substituting variables and routing requests across LLM providers. Results are exported as structured files with generated content, metadata, and generation statistics.

Organizes generated content and images into projects with hierarchical folder structures, tagging, and metadata tracking. Each project maintains a history of generated assets, templates used, and generation parameters. Users can organize content by campaign, client, or content type, and search/filter assets by tags, date, or generation parameters. The platform tracks which template and LLM provider generated each asset, enabling reproducibility and quality analysis.

Maintains a library of pre-built and user-created templates for common content types (blog posts, social media, product descriptions, emails, etc.). Templates include variable placeholders, system prompts, model selection rules, and output formatting. Users can create custom templates, save successful prompts for reuse, and share templates within teams. The platform tracks template performance metrics (average generation time, user satisfaction ratings) to help identify high-performing templates.

Tracks API costs and token usage across all configured LLM providers, providing detailed breakdowns by project, template, and provider. The platform calculates cost per generation and identifies cost optimization opportunities (e.g., 'this task could use a cheaper model without quality loss'). Users can set budget limits per project or team, and the system alerts when approaching limits. The cost dashboard shows historical trends and cost-per-output metrics to help teams optimize spending.

Exposes REST API endpoints allowing developers to integrate Cabina AI's text and image generation capabilities into custom applications and workflows. The API supports the same multi-LLM routing and template-based generation as the web interface, with authentication via API keys. Developers can submit generation requests, poll for results, and retrieve generated content programmatically. The API includes webhook support for asynchronous processing and batch job status notifications.

Enables multiple team members to work on shared projects with granular permission controls. Admins can assign roles (viewer, editor, admin) to team members, controlling who can create/edit templates, generate content, and manage billing. The platform tracks who generated each asset and when, maintaining an audit log for compliance. Team members can share projects and templates within the organization, and admins can enforce organization-wide policies (e.g., required templates, approved providers).