Qverlabs founder Abhi Anand shares how his experience at PwC and with global banks influenced the company's approach to building compliant, enterprise-grade AI.
Before founding QverLabs, I spent 17 years working at the intersection of technology and financial regulation, first at PwC and then directly with global banking institutions. That experience fundamentally shapes how we think about building AI products. When you have seen what happens when technology fails in regulated environments, you develop an appreciation for reliability, auditability, and compliance that is hard to acquire any other way.
Lessons from Regulated Industries
Banking taught me that clever technology is necessary but never sufficient. Every system must be auditable, meaning you can trace any output back to its inputs and the rules that produced it. Every change must be documented. Every failure must be recoverable. These principles, drilled in through years of regulatory examinations and compliance reviews, directly inform how we architect our AI systems at QverLabs.
I also learned that the most dangerous risks are the ones you do not know you have. In banking, this means hidden correlations in trading books or undocumented data flows between systems. In AI, it means model behaviours you have not tested for or data processing activities you have not mapped. Our approach emphasises comprehensive testing and continuous monitoring precisely because of this lesson.
From Banking to AI Startups
The transition from financial services to AI entrepreneurship might seem like a large leap, but the core challenges are remarkably similar. Both domains require building systems that handle sensitive data responsibly, make consequential decisions at scale, and operate within evolving regulatory frameworks. The compliance automation platform we built at QverLabs is a direct result of experiencing the pain of manual compliance processes firsthand.
Building for Trust
Perhaps the most important lesson from banking is that trust is earned slowly and lost quickly. A single compliance failure or data breach can undo years of reputation building. This is why we take a conservative approach to AI autonomy, always ensuring that human oversight is available for high-stakes decisions. It is also why we invest heavily in security, testing, and transparency. In regulated industries, there are no shortcuts to trust, and we believe the same principle applies to AI products serving any sector.
