While Python is often considered the primary language for AI, Ruby on Rails offers an effective and efficient approach to developing AI-powered applications. The most impactful AI solutions often come from applying proven AI to real-world problems, not necessarily from groundbreaking AI research. At SINAPTIA, we leverage the speed of Ruby on Rails and the strategic integration of existing AI models to deliver intelligent applications that drive business results.
Key advantages for AI application development
Building applications is an iterative process of experimentation, prototyping, and refining AI model integration based on performance and user feedback. Even more so now that the AI landscape is boiling with changes and evolution. The ability to quickly evolve a prototype is crucial for this process and Ruby on Rails is well-known for its developer productivity and speed, making it a perfect match for the iterative nature of AI application development. These are some of the key advantages it offers:
Convention over configuration
Ruby on Rails’ “Convention Over Configuration” philosophy reduces the need for extensive configuration by making sensible assumptions about application structure. This enhances collaboration and knowledge sharing among developers.
Rich ecosystem
The vast RubyGems ecosystem provides pre-packaged libraries (gems) for various tasks, eliminating the need to build common features from scratch. Gems are available for authentication, admin interfaces, front-end integration, and more. This accelerates development, allowing teams to focus on unique AI application aspects.
Built-In Tools and Libraries
Ruby on Rails includes built-in tools and libraries. Besides tools for common web development tasks like routing, database interaction, HTML rendering, and testing, Ruby on Rails 8 introduced a suite of powerful, built-in tools designed to enhance application performance and simplify development: Solid Queue, Solid Cable, and Solid Cache. These “SOLID” tools in Rails provide us with everything we need to build robust applications quickly, without needing any external services.
Strategic AI Integration
Powerful, pre-trained AI models are now widely accessible through APIs from major AI providers. For many AI applications, the innovation lies in effectively integrating these models to solve specific problems and enhance user interaction, rather than developing new AI algorithms. Providers like OpenAI (ChatGPT, DALL-E), Google (Gemini), and Hugging Face (Transformers) offer well-documented APIs for easy integration. Rails developers can use their existing skills to incorporate these AI capabilities without deep machine learning expertise. The focus is on effective prompting, processing AI output, and building user-friendly interfaces.
Gems for AI Integration in Rails
The Ruby on Rails ecosystem offers various gems that simplify AI integration by providing:
- Wrappers around AI provider APIs (OpenAI, Anthropic, Gemini, Hugging Face, ollama)
- Multi-provider libraries (openrouter, ruby-llm)
- Vector databases clients (sqlite-vec, pgvector, weaviate, qdrant, chroma, pinecone, milvus)
- Higher-level frameworks (raix, langchainrb, sublayer)
- Model Context Protocol integration (fast-mcp, action_mcp)
- Lower-level AI libraries (informers, transformers, neighbor, torch-rb)
Our experience with AI Applications in Rails
The combination of rapid Rails development and easy AI integration enables various applications. We’ve successfully helped customers to implement:
- Image validation and classification workflows
- Data-entry validation and data enrichment
- Proofreading, misspelling, and enhancement for user-provided documents
- Text splitting and classification of image+text data
Building the next Intelligent application
Ruby on Rails offers a streamlined path to integrate intelligent features, focusing on your application’s core value and user experience. If you want to explore AI-powered possibilities with Rails, get in touch!