AI-Driven Language Practice with Workflow-Based Feedback
LoopLingo is an AI-powered language learning platform designed to solve a key limitation of existing apps like Duolingo: limited practice depth and shallow feedback.
Instead of daily caps and generic responses, LoopLingo provides unlimited CEFR-aligned practice and uses Kestra workflows to asynchronously analyze learner performance and deliver targeted feedback.
The frontend is deployed on Vercel for fast, global access.
Most language learning platforms suffer from:
- Limited daily practice sessions
- No session-level mistake analysis
- Synchronous, blocking feedback that hurts user experience
This makes them ineffective for:
- Serious learners
- CEFR / DELF preparation
- Entrance exams and grammar-heavy evaluations
LoopLingo introduces a workflow-driven architecture:
- Unlimited AI-generated exercises aligned to CEFR units
- Real-time scoring and mistake tracking
- Asynchronous feedback via Kestra workflows
- Fast, production-grade frontend deployment using Vercel
Feedback generation is offloaded to Kestra, ensuring the learning flow is never blocked.
Kestra is a core architectural component, not a background utility.
- Webhook-triggered workflows at the end of learning sessions
- Independent AI analysis of user mistakes
- Workflow-based feedback generation
- Callback delivery of results to the application
- Non-blocking feedback
- Observable and retryable executions
- Easy extension to reports, emails, PDFs, or LMS systems
This aligns naturally with Kestra’s event-driven orchestration model.
Vercel is used for:
- Instant frontend deployment
- Automatic CI/CD from GitHub
- Global edge delivery for low latency
Judges can access the platform immediately without setup.
- React
- Axios
- Vercel
- Node.js
- Express
- Groq API (LLMs)
- Kestra (Webhook-triggered workflows)
- Frontend: Vercel
- Backend: Render
- Kestra: Local (demo), cloud-ready
User ↓ Vercel Frontend (React) ↓ Render Backend (Node.js / Express) ↓ Kestra Webhook Trigger ↓ Kestra Workflow ├─ AI Feedback (Groq) └─ Callback to Backend ↓ Frontend Notifications
Key idea: AI feedback is fully decoupled from the user interaction flow.
Each learning session triggers a Kestra workflow that:
- Receives session data via webhook
- Generates concise, targeted feedback using an LLM
- Sends results back to the application through a callback API
This ensures:
- Faster perceived performance
- Fault isolation
- Clear execution visibility
- Frontend is fully deployed on Vercel
- Backend is deployed on Render
- Kestra runs locally during the demo
- Ensures deterministic execution during judging
- Avoids free-tier cloud instability
- Workflow is production-ready and cloud-portable without code changes
This is an intentional engineering decision.
LoopLingo is structured strictly around:
- CEFR levels (A1, A2, B1, etc.)
- Unit-based grammar and vocabulary progression
- Difficulty scaling per unit
Currently, French is implemented end-to-end to demonstrate depth and correctness.
The architecture supports adding additional languages without redesign.
- Multi-language expansion
- Exam-oriented practice modes (DELF, IELTS-style grammar)
- User accounts and progress analytics
- Cloud-hosted Kestra for large-scale orchestration
- Institutional usage (schools, coaching centers)
- Automated performance reports and revision plans
- Backend and workflows are container-ready
- Services are loosely coupled
- Architecture supports migration to cloud VMs or managed workflow services
LoopLingo demonstrates how Vercel and Kestra can be combined to build a real-world, scalable learning platform — not just a demo application.
The focus is on:
- Real problems
- Correct architecture
- Sponsor-aligned tooling
- Production-ready design