Zoa Research

Zoa Research employs large language models (LLMs) embedded in optimization loops to automate the modeling cycle. By leveraging data across various contexts, it reduces reliance on human intuition, allowing for more efficient forecasting and improved selection of experiments.

Zoa Research utilizes sample-efficient general models and large language models (LLMs) to create powerful quantitative forecasting models. These technologies enable the automation of the build-test-improve cycle, enhancing the forecasting process.

Key features of Zoa Research include the ability to automate the modeling cycle, leverage data from diverse contexts, and improve forecasting accuracy. This approach allows for the identification of significant experiments with high expected uncertainty reduction.

Zoa Research employs large language models (LLMs) as part of its AI technology. These models are designed to generalize across domains and enhance forecasting capabilities through iterative updates and quantitative evaluations.