A guided AutoML Path to Faster, Better Data Insights

Rulex’s AutoML streamlines and accelerates data analytics with a user-friendly, guided workflow.

Areas of application

FINANCIAL SERVICES

Fraud detection
Credit scoring
Early warning

SUPPLY CHAIN

Inventory planning
Route optimization
Demand forecasting

MARKETING

Churn prediction
Product cross-selling
Multi-channel sales and marketing analytics

ENERGY

Predictive maintenance
Energy production forecasting

Rulex’s AutoML

By automating typically labor-intensive tasks like data preparation, pre-processing, and feature selection, this tool empowers both seasoned data scientists and citizen developers to seamlessly experiment with multiple machine learning models.

Advanced customization features and tasks are integrated in a highly eXplainable flow, allowing users to analyze results throughout the process for quick modifications and informed decision-making.

Rulex's AutoML streamlines and accelerates data analytics with a user-friendly guided workflow

Why Rulex’s AutoML is different

Quick set-up

Rulex’s AutoML can be configured to address classification and regression problems in the blink of an eye. By importing data for analysis and specifying the desired target outcome, the tool autonomously orchestrates the entire workflow, handling complexity while keeping users in contro.

The automated process includes data preparation and pre-processing, algorithm selection based on the input data, model generation, and application to new datasets. This allows teams to focus on analyzing results and extracting value rather than managing technical overhead.

Explainability by design

Unlike many automated machine learning solutions, Rulex AutoML is built around explainability. Every task and its outcomes can be visualized at each step of the flow, keeping you in control of the process, and facilitating the identification of critical points where adjustments might be necessary.

This clear and interpretable approach facilitates collaboration between data scientists and business experts and supports compliance requirements in regulated industries such as financial services.

Flexible experimentation for classification and regression

Our AutoML’s swift deployment phase allows users to promptly observe predictions and easily evaluate the performance of different machine learning models.

For advanced use cases, Rulex Platform provides more than 100 customizable variables, and a vast selection of algorithms tailored to different business objectives, including:

Logistic Regression
Decision Trees
Random Forest
Support Vector Machines
Neural Networks
Logic Learning Machine (LLM)
SuperLLM

Turning predictions into actionable recommendations

Beyond generating predictions, Rulex’s automated machine learning helps users understand how outcomes can be influenced. By analyzing feature contributions and decision logic, teams can explore alternative scenarios, test strategies, and make informed, data-driven decisions.

Rulex’s automated machine learning in action

See real-world success stories with Rulex’s AutoML.

DEMAND FORECASTING

A global pharmaceutical company needed more accurate demand forecasts for several flagship products. The goal was to generate reliable short-term predictions by combining historical sales data with external variables such as temporal trends, the spread of seasonal viruses, and geographic information.
To address this challenge, Rulex implemented an end-to-end forecasting solution encompassing data pre-processing, modeling, and forecasting. Sales data from different products and external variables were imported, integrated, and cleansed through user-friendly no-code tasks. Rulex Platform’s AutoML then automatically selected the best-fit model and enabled parallel testing of multiple strategies. The company’s domain experts could monitor the process and make adjustments at each stage, thanks to the inherent transparency of the technology.
The solution delivered demand forecasts over a three-month horizon, updated dynamically to incorporate new data and ensure that the latest trends were always factored in. This resulted in accurate forecasts, smooth integration into inventory and logistics systems, and a process that supported better decision-making.
Fully integrated with the company’s existing systems, the forecasts were seamlessly incorporated into downstream processes such as inventory replenishment and logistics planning.

Average error below 10%

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