TrustableAI was an My first startup effort focused on operationalizing responsibility and risk controls in machine learning deployment pipelines.
Through its product Augit, the company explored integrating fairness testing, dataset versioning, model documentation, compliance-oriented certification concepts, and early notions of AI insurance into a unified CI/CD workflow for machine learning systems. The objective was to determine whether deployment readiness could be assessed and enforced through engineering processes rather than ad-hoc review.
This work revealed a critical limitation: without executable semantic constraints, many assurances remained procedural rather than enforceable. Fairness metrics, documentation, and compliance checks could signal intent, but not guarantee behavior under deployment conditions.
TrustableAI is documented here as a practical attempt to gate execution through engineering infrastructure, preceding later work that shifted from process-based assurances toward semantic testing, falsification, and executable guarantees.