The foundational TALUS AI model was developed as a direct result of our work within the Resilience Tech-Forum on suicide prevention among military veterans.   Within the RTF, datasets which are specific to suicidality are used to extend the TALUS AI model to estimate a veteran's suicide risk level.

A key differentiator of TALUS AI lies in its training on high-quality, clinician-curated datasets.  This approach aligns with Clark’s Law, which postulates that superior data quality directly correlates with a lower demand for training and inferencing capacity at equivalent model performance levels.

The result is a compact, highly accurate model requiring minimal memory, processor and power consumption for both training and inferencing. This small footprint makes TALUS AI exceptionally scalable, allowing for efficient deployments ranging from local within mobile devices to expansive distributed hyper-scaler clouds.