Maveric Systems, a banking-exclusive technology specialist, announced the launch of PULSE_AI a continuous quality intelligence platform designed to help banks and financial institutions strengthen trust, governance, and reliability in AI-driven software development and delivery.

PULSE_AI addresses this challenge by embedding intelligence across the software development lifecycle, enabling technology teams to continuously assess, predict, and improve software quality at scale. From requirements analysis and test design to regression planning, execution, defect management, and reporting, the platform helps banks build quality into every stage of software delivery while improving speed, efficiency, and governance.

Built for AI-driven software delivery, PULSE_AI is designed to help banks achieve up to 100% test coverage, a 40% acceleration in release cycles, and a 35% reduction in QA costs. The platform gives banks a faster, more cost-efficient path to releasing trusted software at scale.

"At Maveric, we believe trust will be the defining factor in the next era of AI-first banking, and trust cannot be built through experimentation alone; it requires engineering discipline at scale," said P.Venkatesh, Co-founder, Maveric Systems. "As banks accelerate their modernization agendas, they need to balance speed with resilience, compliance, and stakeholder trust. PULSE_AI reflects our commitment to helping financial institutions embed quality into every stage of software delivery and innovate with greater confidence."

AI-first banking requires quality engineering that is as intelligent, connected, and continuous as the systems being built. PULSE_AIbrings AI-native knowledge management, domain-aware regression intelligence and low-code automation into one platform, helping banks map dependencies, prioritise risks and test complex systems at speed. The result is faster delivery, stronger governance, and greater confidence in every release.

PULSE_AI is grounded in a banking-aware knowledge fabric, ensuring every output is shaped by real institutional context. Closed-loop intelligence drives regression, AI-led test automation, and reporting, while enterprise-class observability provides the visibility and control banks need to manage outcomes at scale.