Foresight by Lightning Rod

Foresight models are developed using a Future-as-Label training approach that emphasizes learning from real-world outcomes. This results in higher Brier Skill Scores on resolved prediction questions compared to leading general models. The system supports research features that automatically gather relevant context to inform each forecast. The API extends standard chat completions with parameters for probabilistic answers and research integration. It enables efficient deployment in agentic workflows due to lower inference costs per token. Performance benchmarks show it outperforming alternatives across accuracy and pricing metrics. Common applications include building prediction market bots, risk forecasters for supply chain or policy events, and quant signal trackers that incorporate calibrated probabilities as features. The service targets enterprise, government, and startup users seeking reliable forecasting tools.