GenAI and Causal AI
Maximising GenAI potential for Data Scientists
Ground LLMs in causality, enhance causal discovery, and automate data science workflows to minimize hallucinations, maximize accuracy, and save you time.
Making LLMs work in the enterprise
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Making the most of LLMs using Causal AI
Using causal models to enable LLMs to work with quantitative data.
Extracting domain knowledge for Causal Discovery automatically in both public and private data.
Building and deploying applications faster with a data science co-pilot.
Grounding GenAI/LLMs with Causal AI
Causal models can enable LLMs to ground their answers in accurate quantitative and qualitative statements. The LLM performs verifiable (citable) calculations by calling on the causal model enabling the LLM to work with quantitative data, perform accurate “what-if” scenario analysis, root cause analysis, effect estimation and much more
By grounding responses to statements of a causal nature, the LLM can correctly make statements related to the underlying business model, ensuring generated answers are trustworthy and explainable.
Enhanced Causal Discovery
LLMs can help identify cause-and-effect relationships within your data by automatically mining for causal knowledge and proposing this domain knowledge to the user. By combining this with causal discovery algorithms, more accurate causal graphs can be developed quickly and with a lot less effort.
Fly Faster with a Causal Co-Pilot
Build faster with a data science co-pilot augmented by the decisionOS code base and documentation. Your co-pilot supports you in your use case throughout the full causal workflow – from causal discovery to decision intelligence. Build your enterprise decision apps faster, more accurately and with much less effort.
Dive deeper into the opportunities unlocked by GenAI and Causal AI
Download our whitepaperExperience the power of Causal AI and Gen AI together
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Talk to our team, book a personalized demo and see AI decision-making in action.
Get startedDownload our whitepaper
Read our whitepaper u0022Large Language Models and Causal AI: Synergies and Opportunitiesu0022 to find out more about agentic causal agents working with LLMs.
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