Build & deploy predictive models, extract causal insights and much more with a few clicks

1. Connect: Upload Excel & CSV file or connect to your SQL database

2. Transform: Transform your data and feature engineer with our SQL editor

3. Analyze: Extract actionable insights from one of our Machine Learning or statistical tools

4. Visualize: Share your results with a rich set of visualizations & dashboards

5a. Schedule: Schedule to get & export fresh results from the latest data

5b. Integrate: Use the trained model's RESTful API to integerate with your own applications

Available Analytics

A full list of features from our advanced analytics and more are coming soon

Predictive modelling

Trains predictive models with labelled data to predict unlabelled data

Causal Discovery

Automatically detects causal graph (DAG) with input constraints from domain experts

Including the most common algorithms such as PC, DirectLinGAM, NOTEARS and the latest deep-learning based DECI
Domain experts can add constraints and hints to guide the algorithms to discover only causal graphs that satisfied specified conditions
Interactive and visualizations of the discovered DAG so the user can easily understand the causal effects and fine-tune the constraints until entirely satisfied

Timeseries forecasting

Forecasts future by capturing temporal patterns from historical data

Automatically trains and selects the best model from a variety of algorithms including statisitcal, tree-based and Deep Neural Networks
Configurable model and hyperparameter search
Works seamlessly with additional numerical or categorical features
Handles both univariate and multivariate timeseries
Works seamlessly with timeseries groups for cross learning between groups

Causal inference

Estimates causal effect from observational data without expensive and time-consuming RCTs or A/B testings

Uses Double Machine Learning with AutoML for robust inference while minimizing biases
Estimates both ATE and CATE for heterogeneous effects
Works seamlessly with categorical and text columns
Handles both categorical and numerical treatments
Handles both categorical and numerical outcomes

Segmentation‍

Clusters customers or products to similar cohorts with a rich set of features

Works seamlessly with categorical columns
Captures complex relationships and learns item similarity in high dimensional space with Deep Learning
Includes cluster explanations in natural language
Includes prediction explanations with Shapley values

Counterfactual predictions (What if?)

Predicts potential outcomes when a hypothetical intervetion is introduced

Uses Causal Inference for correctly estimating causal effect and potential outcomes
Include intervention recommendations for given expected outcomes for optimisation

Sentiment analysis

Extracts keywords and their sentiments from 12 different languages with ease

Statistical analyses & visualizations

Our toolbox also contains different hypothesis testing tools and statisitcal analyses

Includes hypothesis testing methods such as ANOVA, parametric and non-parametric methods
Includes statistical analyses such as correlational analysis, Bayesian Linear Regression, Association Rules, etc.
Includes a rich set of visualizations for exploratory data analyses

Low-code AI & Advanced Analytics