Introduction

Neuralk AI develops powerful Tabular Foundation Models (TFMs) purpose-built for industry applications, delivering state-of-the-art accuracy on real-world ML tasks across commerce, finance, healthcare and beyond.

NICL (Neuralk In-Context Learning)

NICL is our foundation model designed for tabular prediction tasks. It uses In-Context Learning to make predictions based on examples you provide, without requiring traditional model training or hyperparameter tuning.

Zero Training

No hyperparameter tuning needed. Strong baseline performance out of the box.

Fast Iteration

Explore new datasets quickly. Adapt to changing data without retraining.

Scikit-learn Compatible

Familiar fit/predict interface. Works with pipelines and cross-validation.

Flexible Deployment

Cloud API or on-premise. Same SDK, same interface.

Using the Model

There are 2 different ways to use NICL, through a familiar interface compatible with scikit-learn.

Use NICLClassifier with your API key:

from neuralk import NICLClassifier

clf = NICLClassifier(api_key="nk_live_xxx")
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Use NICLClassifier with the host parameter:

from neuralk import NICLClassifier

clf = NICLClassifier(host="http://your-server:8000")
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

→ Learn more about On-Premise Deployment

Getting Started

  1. Install the SDK: pip install neuralk

  2. Get your API key: Run neuralk login

  3. Make predictions: See Quickstart

Learn More