Industries

Transform software decisions with AI

From churn prediction to business operations, TabPFN handles any prediction in seconds, not sprints.

Ship predictions as fast as you ship features

TabPFN delivers instant predictions for hundreds of use-cases from customer behavior, business operations, and product decisions, eliminating your ML bottleneck with structured data.

One model for all purposes

Replace dozens of specialized models with one foundation model that handles any prediction task


Production-ready out of the box

Integrate your product through a simple API — deploy sophisticated predictions in an instant without having to maintain complex retraining pipelines

Zero-shot predictions

Get institutional-grade results immediately — no data prep, no model selection, no long training runs & no retraining needed

Experiment and iterate instantly

Test new prediction approaches in minutes, not months — perfect for A/B testing and rapid product experimentation

High impact use cases

Customer Segmentation: Retail & Marketing

This dataset contains 2,241 customers with demographics, purchasing behavior, campaign responses, and channel preferences to identify distinct customer segments for targeted marketing.

TabPFN achieves 99.5% ROC-AUC without tuning, significantly outperforming CatBoost (93.9%), LightGBM (92.9%), XGBoost (89.9%), and Random Forest (86.2%).

Retailers can segment customers continuously as new behavioral data arrives. Marketing teams personalize campaigns by segment, allocate budgets to high-value customer groups, and test new product launches without rebuilding segmentation models for each campaign.

Customer Churn: Telecommunications

This dataset contains 7,000 telecom customers with service usage, contract terms, billing history, and demographics to predict which customers will churn.

TabPFN achieves 99.9% ROC-AUC without tuning, significantly outperforming Logistic Regression (96.3%), CatBoost (89.8%), LightGBM (89.1%), and XGBoost (80.8%).

Telecom providers can score churn risk continuously across their customer base. Retention teams target at-risk accounts with tailored offers, reduce involuntary churn through proactive outreach, and adapt to seasonal patterns without retraining models quarterly.

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