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Blogs, product updates, research, and customer stories from Synthefy.
Introducing Nori-Rel: predictions for your relational databases
Nori-Rel turns prediction queries over relational databases into results, handling data preparation and feature construction with Nori's pretrained foundation model.
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Predicting NFL Passing Yards with Nori: A 24% Backtest Return
We used Nori to predict NFL passing yards and price Kalshi bets. A 2025 quote-based backtest returned 24.1% after fees and modeled execution costs.
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Synthefy's 2026 Fantasy Football Rankings: Predictions from 1,900 Player-Seasons of Data
Synthefy used Nori and 1,900 historical player-seasons to forecast and rank the top 200 fantasy football players for 2026.
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From Time Series to Operational Intelligence: Synthefy Nori Tabular Foundation Model on InfluxDB
Time series data runs the operational world. Organizing it is the first step. Intelligence is the next step.
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Nori-Rel, Scaling Beyond a Million Rows, Explainability, and Nori on Mac
Nori leads RelArena with Nori-Rel, scales beyond a million rows, adds feature importance, and now runs on Apple Silicon GPUs.
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Building Foundation Models for the World's Structured Data
We're announcing our $6.5M seed round, led by Wing Venture Capital, to build Structured Data Foundation Models for the numerical data that runs the world.
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From Black Box to Glass Box: Extracting Interpretability from Nori
Nori is a black-box tabular foundation model, but it separates each feature's contribution so cleanly that we can extract its reasoning and rebuild it as a transparent glass-box model you can read.
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Better Predictive Maintenance with Nori
Learn what predictive maintenance is and how to use Nori to predict whether equipment will fail within an upcoming maintenance window.
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Better Remaining Useful Life Prediction with Nori
Learn how to use Nori to estimate how much operating life remains from a machine's sensor history.
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Better Cold-Start Forecasting with Nori: New and Short-History SKUs
Forecasting a product that has no sales history of its own, by borrowing demand patterns from the SKUs it resembles.
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Introducing Nori Flash: Nori's Accuracy, Now in Microseconds on CPU
Nori Flash distills our tabular foundation model into a compact MLP — keep Nori's zero-training accuracy, but run inference on CPUs in microseconds, thousands of times faster and cheaper than a foundation-model forward pass.
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Introducing Nori Embeddings: Representations That Know What You Care About
We're releasing programmatic access to Nori's embeddings: target- and context-aware vectors for tabular rows, pulled straight from the pretrained foundation model. They unlock search, retrieval, interpretability, and more, far beyond regression.
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Synthefy-Nori: The Foundation Model That Replaces XGBoost
Train nothing, predict anything. Synthefy-Nori-V1 is the only fully open-source tabular foundation model — 6M parameters, zero training, and #1 mean R² across a 96-dataset regression benchmark.
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Synthefy MUSEval: The Largest Multivariate Evaluation Benchmark for Time Series Foundation Models
MUSEval is the first large-scale benchmark (45 datasets, 19B points, 16 domains) built to measure multivariate gain — how much better models get when given related signals.
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Why LLMs Can't Solve Time Series
Discover why Large Language Models struggle with time series forecasting and what the industry needs instead.
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"DALL-E" for Timeseries: Scaling Time Series ML with Synthetic Data Generation
Learn how synthetic data generation is revolutionizing time series machine learning, just like "DALL-E" transformed image generation.
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Introducing Synthefy API: State-of-the-Art Time Series Forecasting for Everyone
Discover how Synthefy API brings cutting-edge time series forecasting capabilities to developers and businesses of all sizes.
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Data Enrichment: The Missing Ingredient in Time Series Modeling
Explore why data enrichment is crucial for improving time series model accuracy and how to implement it effectively.
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Recognition & milestones
One of the best on leading time series benchmarks for univariate forecasting
Best performer on multivariate and multimodal time series benchmarks (Coming Soon)
US Army Phase 2 SBIR award recipient
Advancing time series AI for defense and national security applications