Clients can now access this world-first innovation on the AI Insights Platform or as a downloadable model, seamlessly integrating it into their machine learning workflows. It strengthens investment strategies by addressing look-ahead bias in financial forecasting using time-boxing—a technique ensuring insights are based only on data available at prediction time.

Enhancing return forecasts
aisot’s models are fine-tuned quarterly, leveraging an encoder-based transformer specifically optimized for financial tasks such as return predictions. These sentiment factors, derived from LLMs, enrich expected return forecasts in quantitative models, offering a more accurate outlook on future performance.

Mitigating biases
To prevent hallucinations and minimize look-ahead bias, aisot uses time-boxing and integrates LLM outputs with quantitative models under strict portfolio constraints. By combining sentiment data with Bayesian frameworks, aisot ensures reliable, real-time insights that align with client-specific risk models, delivering actionable investment intelligence.