Defense
Phase II SBIR award recipient
Advancing time-series AI for defense and national-security applications.
Nori V1 · open-source & live
Train nothing.Predict anything.
One pretrained model for any tabular prediction. No training, no tuning.
The world runs on predictionsBillions of shipment and routing forecasts run every day.
Trusted by leading companies
Adopted in production
Trusted across industries
From semiconductors to defense, teams ship real outcomes on Synthefy — backed by peer-reviewed research and public benchmarks.
Healthcare · Edge AI
40% better model efficiency on-device
Real-time heart-rate AI running on bare-metal microcontrollers — 300× smaller than baseline models.
Read case study
Enterprise infrastructure
Reinventing time-series intelligence
How Synthefy partners with NetApp to bring foundation models to enterprise data.
Read the story
Telecom
Selected for the T-Challenge
Backed by one of Europe's largest telecom innovation programs.
Read interview
Research
Paper accepted to NeurIPS 2025
Peer-reviewed research in multivariate time-series synthesis and forecasting.
Read our research
Benchmark
Among the best on GIFT-Eval
A leading public benchmark for univariate time-series forecasting.
View benchmark
Why this matters
The world runs on tables.
The predictions that actually run a business — credit decisions, fraud flags, demand, pricing, churn, capacity — aren't made from prose or pixels. They're made from rows and columns. Tabular data is the most valuable data most companies own, and the hardest to get right.
Banking & risk
Credit scoring, fraud detection, and lifetime-value prediction from customer and transaction tables.
Retail & supply chain
Demand forecasting, pricing simulation, inventory planning, and scenario analysis.
Insurance
Claims severity, underwriting risk, and churn — straight from policy and claims tables.
Healthcare
Readmission risk, cost prediction, and triage from structured clinical records.
Infrastructure
Capacity planning, throughput forecasting, incident risk, and predictive maintenance.
Growth & marketing
Conversion, lifetime value, and propensity scoring across the funnel.
The problem
Tabular AI is stuck in the past.
Most teams still reach for gradient boosting, and every new dataset starts from zero — explore, engineer features, pick a model, tune it, validate it, then stand up the MLOps to keep it alive. The data drifts, and you run the whole gauntlet again.
- 01Explore the data
- 02Engineer features
- 03Select a model
- 04Tune hyperparameters
- 05Cross-validate
- 06Retrain on drift
And again, from the top, every time the data shifts.
The shift
Every other domain got a foundation model. Tables just got theirs.
Pretrain once, use everywhere, never train per task — that's what foundation models did for text, images, and audio. Tables never had one. Now they do.
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.
Before
Traditional ML stack
ARIMA
CatBoost
Feature engineering
Training and tuning
Offline evaluation
Retraining and MLOps
After
Synthefy foundation model
from synthefy_nori import NoriRegressor
model = NoriRegressor()
model.fit(X_train, y_train)
predictions = model.predict(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
Available via API, with SOC 2 Type II, HIPAA, GDPR, and Zero Data Retention
Businesses
For Business Teams
Upload data, access analytics, and inform business decisions with Synthefy Platform.
Developers
For Developers
Open-source foundation models for tabular prediction and forecasting in your applications.