Attune required a platform capable of:

➡️ Real-time time series forecasting
➡️ Automated anomaly detection
➡️ Flexible model training and management
➡️ High visibility into model performance

The solution needed to integrate seamlessly with their existing data streams and offer both on-demand training and continuous inference, all within an intuitive and lightweight interface.

We designed and delivered a minimum viable product (MVP) optimized for speed, low deployment cost, and user-friendliness. Key components included:

✅ Interactive dashboard with model selection, 14-day historical view, 24-hour forecast, and visual confidence intervals
✅ Model training workflow via CSV upload, column mapping, regressor assignment, and sensor channel configuration
✅ Model management features including versioning, retraining, disabling, and deletion
✅ Modern tech stack: Python (FastAPI), Streamlit, NeuralProphet, PostgreSQL + TimescaleDB, Docker, Redis/Celery
✅ Automation pipelines for hourly forecasting and daily retraining (configurable)

✅ Fully functional MVP deployed on Attune’s infrastructure
✅ Lightweight UI for configuring models and monitoring forecasts
✅ Dynamic integration with their existing database for selecting channels
✅ Automated retraining and forecasting workflows

✅ MVP deployed and operational in under 10 business days
✅ Validated by the client as “intuitive and effective"
✅ Initial models trained and produced accurate forecasts within the first week
✅ Attune can now independently iterate on forecasting models without relying on external support