Before
Traditional ML stack
ARIMA
CatBoost
Feature engineering
Training and tuning
Offline evaluation
Retraining and MLOps
Foundation Models for Structured Data
Synthefy turns tables, time series, metrics, and business context into forecasts, scenarios, and decisions teams can build on.
The world runs on predictionsBillions of shipment and routing forecasts run every day.
Trusted by leading companies
Structured data foundation models
Replace the boosting stack with one API call.
Synthefy foundation models remove feature engineering, model selection, and hyperparameter optimization from structured-data prediction.
ARIMACatBoostAfter
Synthefy foundation model
from synthefy import SynthefyNoriClient
client = SynthefyNoriClient()
predictions = client.predict(X_train, y_train, X_test)
No hyperparameter sweepsProduction-ready predictionsLearns from structured signals
6 months
of ML work cut
1 API call
to ship predictions
Beats XGBoost
tuned, on 9 of 13 TabArena regression datasets
Developers
For Developers
Open-source foundation models for tabular prediction and forecasting in your applications.
Recognition & milestones
Performance
One of the best on leading time series benchmarks for univariate forecasting
Best performer on multivariate and multimodal time series benchmarks (Coming Soon)
Award
US Army Phase 2 SBIR award recipient
Advancing time series AI for defense and national security applications
Published
Paper accepted to NeurIPS 2025 conference
Breakthrough research in multivariate time series synthesis and forecasting
Collaboration
Powering real-time health AI on Samsung devices
Breakthrough PPG heart rate monitoring running on edge microcontrollers