NeoSoul

Rubric-forecasting enables the systematic evaluation of qualitative evidence against predefined criteria to produce quantitative probability outputs. It allows users to transform unstructured information into auditable forecasts, ensuring that every prediction is backed by a transparent, rubric-based scoring methodology suitable for high-stakes enterprise decision environments.

This capability is designed for risk analysts, financial forecasters, and strategic planners who require rigorous, evidence-based decision support. It is ideal for professionals tasked with quantifying uncertainty in complex domains where traditional statistical models lack sufficient historical data or require human-in-the-loop evidence validation.

Implementation requires the development of domain-specific rubrics that define the scoring criteria for evidence evaluation. Users must provide structured or unstructured evidence sets that align with these rubrics to generate valid probability outputs, ensuring the input data is relevant to the specific forecasting topic being analyzed.