Causal AI + GenAI
Causal AI + Generative AI = Better Together
Our Approach
Large Language Models (LLMs) have significantly influenced the AI field, making strides in both creative and routine tasks. However, their independent use in enterprise decision-making reveals limitations regarding consistency, transparency, and understanding the causal relationships in data. In contrast, Causal AI provides a clear framework to uncover cause-and-effect relationships, constructing causal models and delivering actionable insights for reliable decision-making.
Our causal ai services
Causal Fine-Tuning
Embedding causal thinking into Large Language Models (LLMs) involves using causal diagrams for visual representation and deeper knowledge comprehension. This integration of human-refined causal assets with LLMs creates unique responses, enhancing response quality and addressing trust issues in generative models. For instance, churn analysis now includes causal drivers due to the embedded causal representation in the LLM.
Advanced Causal Graphs with GenAI
We help our customers uniquely integrate advanced Generative AI with human expertise to revolutionize the creation of causal graphs. By blending sophisticated algorithmic approaches with domain knowledge, we enable the discovery of intricate cause-effect relationships. Our solution accelerators significantly enhance practitioner efficiency by providing initial directional causality suggestions, complete with detailed explanations.
Explainable AI through Causal AI
We help our customers build transparent causal graphs and models, simplified by generative models for easy interpretation by business users. This approach makes complex causal relationships accessible to those without a data science background, facilitating effective enterprise decision-making.
Get Started With AI Experts
Write to us to know more about how we can help you with implementing Causal AI.
