How Navi’s Digital Teams Uses AI to Provide Omnichannel Customer Support

The Sachin Bansal-led Navi Limited (formerly known as Navi Technologies Limited) is a digital-first financial services company, providing app based financial products and services. The company planning to go public in the second half of FY26 aims to increase its Assets Under Management (AUM) to over Rs 50,000 crore. As of FY25, its lending arm Navi Finserv reported a loan book of Rs 11,694.93 crore, and total income of Rs 2,289.9 crore. 

Navi is widening its Artificial Intelligence (AI) led customer support operations in FY26 to resolve the majority of customer queries, resulting in a significant reduction in customer grievances. This transformation has been powered by a combination of inhouse tech and external vendor platforms. 

FE FUTECH speaks with Sugandha Sharma, Head of Operations, Navi.

Excerpts

What were the key customer support operations initiatives undertaken in the 9 months of FY 2025–26?

In FY 2025-26, Navi significantly upgraded its customer support operations by redesigning its support model to be omnichannel and blended, where a customer’s issue is handled seamlessly across chat, phone, and email without loss of context and continuity. 

We also scaled advanced AI-driven support systems that now resolve the majority of incoming queries with speed and accuracy while preserving empathy for complex or sensitive cases. This resulted in much faster turnaround times -- for example, average resolution times of ~5 min for calls and ~10 min for chats – and a marked reduction in escalations and complaint volumes.

What were the challenges faced and how did you overcome them?

Rapid growth in customer volumes posed a challenge in keeping service fast without compromising quality. To address this, we moved decisively from AI experimentation to production-grade automation. Machine learning systems now handle a large share of routine queries end-to-end. However, we deliberately adopted a “human-in-the-loop” framework for high-complexity or emotionally sensitive interactions.

Who was the technology partner for these initiatives, and why were they chosen?

Navi’s CX transformation has been powered by a mix of internal platforms and external technologies chosen for their ability to scale, integrate, and secure customer data flows. The company builds much of its customer-facing logic in-house and adopts best-in-class automation and conversational AI frameworks to deliver channel-agnostic support. While we leverage best-in-class conversational AI and automation frameworks, the core intelligence layer is built in-house. Our data science and engineering teams develop the predictive models that determine how AI systems function. In other words, AI execution is guided by model-driven decisioning rather than static rules. This is a self learning exercise and improves as and when information size and quality improves. 

Tell us something about your digital teams involved, some of the indigenous tech developed.

Navi’s digital teams encompass data scientists, ML engineers, automation experts, and CX architects who focus on building proprietary support platforms and AI-driven bots. For example, Navi has moved its conversational AI beyond simple chatbots to GenAI-enabled systems that resolve a large percentage of queries end-to-end. The organization emphasises tight integration of tech and operations enabling automation, analytics, and live support to work as a unified experience while maintaining high satisfaction scores.

What are your plans for Calendar Year 2026?

In 2026, we are focused on making customer experience more proactive, intelligent, and personalized. This includes:

• Sending timely reminders or in-app guidance when a payment is likely to fail, so issues can be prevented rather than resolved later.

• Providing context-aware responses based on a customer’s recent activity, such as a loan application or repayment attempt.

• Using AI to detect frustration or distress during conversations and seamlessly routing such cases to experienced human agents.

• Better prioritizing urgent or high-impact cases to ensure faster resolution where it matters most.

We are also working toward giving customers greater control over their experience by enabling them to personalize their app journey, choose communication preferences, and select support modes that suit their needs.

Additionally, we are strengthening automated detection across the entire customer journey, so that any issue is identified early and every impacted customer is proactively guided toward the right resolution.

The overarching goal is simple: move from reactive support to intelligent, preventive, and customer-controlled engagement.