Late-stage defect discovery, often rooted in ambiguous requirements, significantly increases remediation costs especially in regulated industries such as fintech. We present ARC-V, a multi-agent AI system deployed at JPMorganChase that shifts quality assurance upstream by operationalising Large Language Models for automated requirement and code verification. ARC-V utilizes specialised agents to (1) assess requirement tickets against organisational standards in order to provide actionable remedial guidance; (2) verify code against requirements in order to predict defects and offer commit-level feedback; (3) continuously monitor agent performance and adoption. Post-production deployment results at JPMorganChase show that ARC-V greatly increased the quality score of user story fields with ‘value statements’ and ‘acceptance criteria’ achieving score improvements of 8.5 and 4 points, respectively. Crucially, ARC-V achieved a 79% early defect discovery rate, identifying the vast majority of production-escaping bugs before testing. These results validate a requirements-centric, AI-driven approach to scalable software quality assurance in complex environments.