Article
eomer presented “eomer: The Operating System for Decision Intelligence” at AI Tinkerers Singapore on 21 April 2026. Open Government Products hosted the event at Lazada One in Singapore.
The talk addressed a common constraint in enterprise prediction projects: the work does not end when a model produces an output. Teams must prepare data, define evaluation rules, inspect uncertainty, compare model versions, and connect approved results to an operational process.
The eomer platform connects these steps through one workflow. A user can prepare a structured dataset, establish a zero-shot baseline, run forecast or regression inference, and inspect prediction intervals. For classification tasks, the same workflow returns class probabilities and evaluation metrics.
This common structure allows teams to apply consistent evaluation principles across different use cases. An energy team may assess price forecasts, while a retail team may assess demand or customer response. The target and metrics differ, but both teams must preserve data-availability rules, use held-out observations, and define acceptance criteria before deployment.
The presentation also distinguished model performance from decision performance. A lower forecast error can support a better decision, but the final value depends on the action, timing, constraints, and cost of an incorrect prediction. Model evaluation must therefore connect technical metrics with the requirements of the relevant decision process.
The talk presented decision intelligence as a controlled path from structured data to an evaluated operational output.
