Defense
Phase II SBIR contract awarded
Selected through the Army’s competitive R&D program to build time-series AI for defense and national-security forecasting.
Train nothing.Predict anything.
Foundation models for structured data.
$pip install synthefy-noriDocsThe 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
Foundation models for enterprise infrastructure
A NetApp partnership putting time-series foundation models to work on enterprise infrastructure 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.
Every other domain got a foundation model. Tables just got theirs.
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
6M params
a tenth the size of the model it beats
1 API call
to ship predictions
Beats XGBoost
tuned, on 9 of 13 TabArena regression datasets
Managed API served on SOC 2 Type II certified infrastructure — HIPAA, GDPR, and zero data retention
Structured + unstructured
All of your data. One prediction layer.
Nori V1 predicts from structured tables. Migas 1.5 is the first foundation model to fuse unstructured text with time series — so notes, events, and market context sharpen the forecast. Together, they cover the data your business actually runs on.
Businesses
For Business Teams
Upload your tables and get forecasts, scenarios, and dashboards in Synthefy Platform — or send us a dataset and we'll run the first analysis for you, free.
Developers
For Developers
Open-source foundation models for tabular prediction and forecasting in your applications.