• $309 Monthly Payment.


  • Clients often integrate the app into their web, cloud, on-premise and edge/IoT infrastructures, enabling multiple use cases, such as fraud detection, customer identification, underwriting, portfolio monitoring and management, risk identification and mitigation, safety and compliance, population health management, operational and systems performance improvement, demand forecasting, e-discovery, revenue management, price optimization, supply chain operations, customer engagement, ratings and reputation, content management, and digital and mobile commerce, among others.


  • Feature engineering (data encoding, text vectorization/embedding, graph vectorization/embedding, and domain specific embedding) and data exploration (correlation heat maps, frequency plots, and data imputation) are also available.


  • Explanations can be customized to provide narratives for individual predictions by outlining the enhancers and inhibitors of each prediction, i.e., how each feature value in the observation affects the prediction outcome.


  • Multiple explainability methods, proprietary (e.g. Causal, Structural, and Domain-specific goden cases/anchors) and open source (SHAP, LIME, Feature Importance, Tree Interpreter, Rule Mining, ICE, PDP, and Correlation Graph), are available to explain global and individual predictions.


  • Multiple Statistical (e.g. Linear Regression and Logistic Regression), Machine Learning (XGBoost, Random Forest, and Decision Tree), and DeepLearning/AI (Artificial Neural Net, Recurrent Neural Net, Convolutional Neural Net, Autoencoder, and Long and Short-Term Memory) models and algorithms are available to fit (train and test) models.


  • App allows users to work with default datasets for fitting and explaining regression as well as binary and multinomial classification models. The app works with structured (numerical, categorical, cardinal and ordinal), text and video data. Users can also upload their own datasets.

Explainable (XAI) Web App