This is a submission for the Redis AI Challenge: Real-Time AI Innovators.
What I Built
CareerFinder is a web application that suggests career paths based on a user's skills, experience, and education.
The app uses a React frontend for an interactive form and a Node.js + Express backend to process inputs.
Predictions are powered by RedisAI with an ONNX-based model for real-time inference.
If the AI model is unavailable, the system gracefully falls back to mock predictions to maintain usability.
Key features:
- Interactive career suggestion form
- Real-time AI predictions via RedisAI
- Mock fallback for offline model scenarios
- Clean, responsive UI built with React
Demo
Live Demo: [coming soon...]
GitHub Repo: [https://github.com/Mr-spiky/CareerFinder
Screenshots:
How I Used Redis 8
CareerFinder uses Redis 8 with RedisAI as the real-time AI inference engine.
- Model Storage: The ONNX model is stored in RedisAI for fast, in-memory access.
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Real-Time Inference: When a user submits the form, the backend sends a tensor to RedisAI, runs the model with
AI.MODELRUN, and retrieves predictions instantly. - Fallback Logic: If RedisAI or the model is unavailable, the backend switches to a mock prediction generator to ensure the app still returns results.
This setup ensures that career suggestions are delivered in milliseconds, providing a smooth, real-time experience.
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