Zensar Targets AI Reliability With New Assurance Platform

The suite addresses the gap between rapid AI adoption and the necessity for governance, replacing outdated deterministic testing with a system powered by 30 automated checks. It focuses on four critical pillars: data quality, model evaluation, trustworthiness, and non-functional performance. By integrating these checks, the platform identifies hallucinations, monitors for bias, and detects drift before models reach production environments.

Manish Tandon, CEO and Managing Director at Zensar, described the move as a shift from experimentation to business-critical deployment, where rigorous assurance becomes a strategic mandate rather than an afterthought. The platform is designed to produce audit-ready artifacts compliant with the EU AI Act, NIST AI RMF, and ISO 42001 standards.

Early adopters report significant operational gains, including 60% faster release cycles and a 40% reduction in model defects reaching production. According to Chief Operating Officer Vijayasimha Alilughatta, these results stem from combining Zensar’s specialized expertise in quality intelligence and data engineering to neutralize the unique risks posed by autonomous agents and generative systems.