U.S. and India-based quality engineering firm QualiZeal said it has developed and rolled out a new tool that is able to cut software testing times by as much as 60%.
The firm is bringing QMentisAI to the market, a new GenAI tool designed to automate around 80% of manual tasks involved in test case design and defect documentation.
CEO Pradeep Govindasamy said the platform is built to be scalable and industry-agnostic, with additional functionality such as synthetic data generation, risk mitigation support, and performance and security testing.
Govindasamy explained that the platform applies large language models, natural language processing, and agentic AI to automate core testing activities such as user story refinement, defect report generation, and test script creation. It also incorporates human-in-the-loop validation to ensure contextual accuracy.
“The platform addresses long-standing challenges in agile software development environments, including inconsistent test documentation, delays in script creation, and coordination issues across globally distributed teams,” he noted, adding that the solution includes 18 capabilities focused on test planning, automation, and defect management.
According to Govindasamy, the tool aims to improve speed and accuracy across the software development lifecycle without compromising quality.
He was keen to stress that “upcoming features are expected to include root-cause analysis and accessibility compliance testing.”
Betting big on GenAI
For QA leaders at banks and financial institutions, Govindasamy said GenAI represents a generational leap in how software quality is ensured at scale.
In fact, this shift is no longer theoretical, he stressed. “AI isn’t just assisting people in testing tasks anymore. It’s becoming autonomous, goal-driven, and capable of acting with intelligence across the lifecycle.”
Historically, automation in QA has focused on repetitive, rules-based tasks: building test cases, running regression suites, and maintaining scripts.
But agentic AI changes that. These systems aren’t just programmed, they learn, Govindasamy said. They identify changes, adjust scripts, heal themselves from errors, and continue executing without human input.
“Imagine a bank updating its online payment flow or changing KYC requirements,” he explained. “Previously, QA teams would manually revise test cases. Now, agentic AI detects the change, modifies test scripts autonomously, executes new tests, and generates a report explaining its actions. That’s a game-changer.”
“We have seen the industry average cost of quality, now around 18%, drop by as much as 5% through AI-led testing.”
– Pradeep Govindasamy
Focusing on financial services, the pressure to reduce time-to-market while maintaining regulatory compliance has never been higher, Govindasamy stressed. Agentic AI is already showing measurable results.
“We’ve seen the industry average cost of quality, now around 18%, drop by as much as 5% through AI-led testing,” he said. “And maintenance effort has fallen from 20% of team capacity to under 5%.”
Release cycles, too, have accelerated. What used to be quarterly, then weekly, is now moving to daily production releases, enabled by DevOps and AI.
“Agentic systems provide full transparency through real-time reporting,” he added. “There’s no need to manually aggregate test data anymore. Teams can move faster with more confidence.”
For banks still relying on traditional QA processes, Govindasamy’s warning is clear: don’t wait to be disrupted.
“This is not about catching up. It’s about disrupting yourself before someone else does,” he stressed, as he urged firms to push for AI readiness, not just within IT, but across leadership.
“This is no longer just a CIO discussion. It’s happening at the board level,” he shared.
AI testing vital
As always in financial services, innovation comes with responsibility. “Especially in regulated sectors like banking or insurance, even minor AI-driven decisions can carry serious consequences,” Govindasamy cautioned.
That’s why testing the AI itself is becoming just as important as using AI for testing.
This shift is already creating demand for AI-specific QA roles, including engineers who can validate agentic systems using techniques beyond traditional models like boundary value or equivalence partitioning.
“We expect to see eight to 10 new job roles emerge just to test and validate AI,” he said. “And these won’t be optional, they’ll be mission-critical.”
According to Govindasamy, the QA space in the early stages of adoption. Full-scale maturity in agentic testing is expected by 2027.
“Right now, we’re training models, customising LLMs, and laying the foundation,” he said. “But by mid-2027, most enterprise QA environments will be agentic by design.”
For younger professionals and digitally native teams, this presents a historic opportunity. “Gen Z testers and engineers are uniquely equipped to lead this shift. They’ll be the ones building careers in entirely new domains,” Govindasamy explained.
As QA teams in financial services are weighing where and how much to invest, the message is increasingly clear: the age of manual testing and rule-based automation is fading. In its place is a new generation of intelligent platforms, testing systems that learn, adapt, and act.
“We’re not just building testing systems anymore,” Govindasamy concluded. “We’re building trusting systems. And the firms that embrace agentic AI today? They’ll be the ones defining software quality tomorrow.”
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