Control the outcome.
Actionable insights for your data.
DataLux is a data science & analytics platform, leveraging causal inference and machine learning for time-series data.
Best of both worlds
Bringing together the best of causal inference and machine learning, allows to go beyond correlation to extract real information from the data, creating an explainable AI solution.
Time-series and signals
Focusing on temporal datasets where the underlying variables generating the data is constantly changing. Correcting biases in these observational datasets. Easily handles large datasets for production use cases.
Estimate the causal effect of designed interventions without a need to conduct randomised experiments. Measure the impact of planned changes.
Example Use Cases
Bring multiple time-series market and inferred data together such as stock exchange tick data, stock market policy actions, related and cross-industry news, alternative datasets to extract causal information about stock markets, macroeconomics and more.
Shape business decisions, product innovations by providing insights and informing key decisions to improve products. Run interdisciplinary A/B experiments across product development, design and engineering from ideation to decision making.
Learn through data with quasi experimentation to improve processes by designing tests, identifying metrics to work with. Conduct exploratory, look-back data analysis of counterfactual data produced inside and outside the platform.