Newgen: Low-Code, Agentic AI, Real Banking Results

Sandeep Hinduja positions Newgen as a low-code platform which means instead of coding from scratch, teams assemble pre-built, tested components to design, test, and ship faster with less risk. The same approach powers Newgen’s AI layer: an AI “data studio” with pre-trained models, drag-and-drop pipelines, and sandboxed builds before production, shortening release cycles and reducing defects.

Hinduja underscores modularity over monoliths. Newgen delivers containerized microservices so banks can deploy narrow capabilities quickly, not multi-year platforms. A cloud-native architecture supports horizontal and vertical scale, letting institutions start small, prove value, and expand without rework.

Operating model matters too. Newgen offers onshore/near-shore/offshore delivery for follow-the-sun support, plus an ecosystem of ~350 partners to sell, implement, and support solutions, giving banks multiple paths to quicker time-to-value.

On agentic AI, as Greg Sullins says winning programs start with specific problems, faster loan approvals, collateral valuation, or simpler business account opening, rather than “AI for AI’s sake.” AI works best embedded in the workflow as a co-pilot with clear human handoffs for validation or override. Governance and explainability from day one, transparent decisions and audit trails, limit regulatory risk and build trust.

Culture is often the harder lift as AI exposes hidden manual work; smaller and mid-sized banks move faster because they carry less technical debt and can test-learn-deploy with tighter executive backing. A “fail fast” mindset keeps momentum when use cases stall.

Sullins closes by casting Newgen as a long-term transformation partner: embed AI end-to-end (not in silos), co-design governance, and iterate as goals and regulations evolve, turning AI from single projects into enterprise capabilities that drive efficiency, faster decisions, and scalable growth without “hiring an army.”