Spralingua - AI Language Learning Platform
Project Overview
Spralingua is an AI-powered language learning platform designed to provide personalized, interactive experiences for learning Spanish, German, Portuguese, and English. Built as a full-stack web application with Flask and PostgreSQL, it leverages Anthropic's Claude AI to offer real-time conversation practice, email writing exercises, and adaptive lessons tailored to each learner's CEFR level (A1-B2).
The platform combines advanced AI engineering with language education expertise to create an intelligent tutor that understands context, corrects mistakes naturally, and adapts to different learning styles. With features like Web Speech API integration for voice input and Minimax TTS for pronunciation practice, Spralingua provides an immersive learning experience that feels authentic.
This project represents a practical application of AI in education, demonstrating how LLMs can be integrated into scalable web applications to serve specialized domains. It's currently live at spralingua.com, serving language learners worldwide.
Key Features
- Multi-Language Support: Learn Spanish, German, Portuguese, or English with full UI translation and culturally-adapted content for each language.
- Casual Conversation Practice: Chat with AI characters (Harry & Sally) who adapt to your CEFR proficiency level (A1-B2) for natural, engaging practice.
- Email Writing Exercises: Practice formal writing with culturally-appropriate scenarios that teach professional communication skills in your target language.
- Voice Input & Output: Speak and listen with Web Speech API for recognition and Minimax TTS for pronunciation practice.
- Real-Time Feedback: Receive instant grammar corrections and suggestions with dual feedback system - quick hints during practice, detailed analysis after.
- Progress Tracking: Track your advancement through structured CEFR levels with 12 topics per level and exercise completion monitoring.
- Personalized Experience: AI characters address you by name and adapt content to your current level and learning goals.
See it in Action
Watch how Spralingua provides real-time German language assistance
Development Process
Spralingua was developed as a full-stack AI application combining Flask web development, PostgreSQL database design, and Claude AI integration. The project evolved from initial concept to production deployment on Railway through careful iteration and user feedback.
The development process included:
- Designing a scalable database schema with SQLAlchemy to track user progress, topics, and exercise completion across CEFR levels
- Creating specialized prompt templates for conversation, grammar correction, and email writing exercises
- Building a responsive web interface with vanilla JavaScript, implementing real-time chat and voice recognition capabilities
- Implementing Flask session management to maintain conversation context across multiple worker processes in production
- Integrating Anthropic's Claude API for natural language understanding and Minimax API for text-to-speech synthesis
- Developing a comprehensive migration system to synchronize local and production PostgreSQL databases
- Deploying to Railway with Gunicorn, handling multi-worker process challenges and ensuring data persistence
Technical Highlights
Python
Core backend language for AI integration and business logic
Flask
Web framework for handling routes, sessions, and API endpoints
PostgreSQL
Relational database for user progress, topics, and exercise tracking with SQLAlchemy ORM
Claude AI
Anthropic's advanced language model for conversation, grammar correction, and feedback generation
Minimax TTS
Text-to-speech API for pronunciation practice and audio feedback
Web Speech API
Browser-based speech recognition for voice input practice
Challenges & Solutions
Multi-Worker Production Environment
Challenge: Conversation history was lost between messages in production due to Gunicorn's multiple worker processes. Each worker maintains its own memory, causing context loss when different workers handle sequential requests.
Solution: Implemented Flask session-based storage for conversation history instead of in-memory storage. Added comprehensive logging with worker PIDs to diagnose the issue, then moved conversation state to sessions that persist across all workers.
LLM API Context Bleeding
Challenge: Making rapid sequential calls to the Claude API caused responses from one call to "bleed" into another, resulting in JSON hint data appearing as conversation responses to users.
Solution: Added strategic delays between API calls and implemented separate client instances for different functional areas (conversation, hints, feedback) to ensure complete isolation and prevent context contamination.
Database Synchronization
Challenge: Keeping local development and Railway production databases in sync, especially for prompt templates and topic definitions that directly affect learning experience.
Solution: Built custom database comparison and synchronization utilities (compare_databases.py, sync_databases.py) for full replication from local to production, ensuring identical behavior across environments.
Cross-Platform Compatibility
Challenge: Windows console encoding issues with Unicode emojis causing application crashes during development and testing.
Solution: Established strict code guidelines: no emojis in console output, using text markers like [SUCCESS] and [ERROR] instead. Documented this as a critical directive for all development. Emojis remain safe for browser-rendered HTML content.
Results & Impact
Spralingua is now live at spralingua.com, providing language learners with accessible, personalized AI tutoring across four languages. The platform delivers real educational value by offering instant feedback, adaptive difficulty levels, and culturally-appropriate content that would traditionally require hiring multiple human tutors.
Key Achievements:
- Scalable Learning Platform: Successfully deployed full-stack application handling real user sessions with conversation persistence, progress tracking, and multi-language support across CEFR levels A1-B2
- Solved Production Challenges: Overcame critical multi-worker environment issues, API context bleeding, and database synchronization challenges that are common in production AI applications
- Cost-Effective Language Education: Provides affordable access to personalized language tutoring, eliminating geographical barriers and making quality language education available 24/7
- Technical Foundation for Growth: Built modular architecture with PostgreSQL database, session management, and AI integration patterns that can scale to support thousands of concurrent learners
The platform demonstrates how modern AI can transform education by combining natural language understanding with structured learning frameworks. Technical lessons learned include managing stateful conversations in distributed systems, preventing AI context contamination, and synchronizing complex database schemas across environments—all essential skills for building production-grade AI applications.