Mastering Quantitative Decision Making

Enterprises run on quantitative, tabular, or time series data. Generative AI (GenAI) systems, particularly large language models (LLMs), work well with language but not numbers. They cannot handle quantitative data natively, and other approaches are needed.

By grounding LLM models with causal AI, you can use your enterprise’s quantitative data to improve the accuracy of your decisions. Using causal discovery, you can create a deep, quantitative understanding of your business that allows you to conduct:

• Scenario analysis – What if we reduced the price of product x?
• Root cause analysis – Why are sales down this month?
• & Causal effect estimation – How can we improve customer retention?

Take charge of your data science productivity by grounding your existing quantitative models. Our AI Agent LLM-powered workflows turbocharge data scientists with domain knowledge co-pilots.

The co-pilots enable rapid causal model and code development, ensuring data scientists can effortlessly generate causal artefacts that can reliably and accurately answer causal business questions.