Cashmere’s Custom Models are built on explicit reasoning rather than opaque pattern matching. Each insight is derived from domain-aware logic that reflects how real decisions are made, allowing teams to understand why a conclusion was reached and iterate confidently as requirements evolve.

Every prediction surfaces the signals and factors that contributed to it, including feature importance and confidence. This transparency makes outputs easier to validate, trust, and act on, which is critical for regulated environments and teams that must stand behind their decisions.

Cashmere is designed to meet the security standards of financial institutions, with SOC 2 Type II compliance, field-level encryption, and VPC isolation protecting both your data and your models. Custom Models operate within strict access boundaries to ensure sensitive information remains secure at every layer.