--- title: "Ekart: Improving NDR recovery, RTO reduction, and lead qualification with Ringg" description: "See how Ekart used Ringg AI voice agents for non-delivery report (NDR) calling, RTO reduction, and business lead qualification across multilingual, high-volume workflows." canonical_url: "https://www.ringg.ai/case-studies/ekart" last_updated: "2026-07-14T10:43:19.000Z" --- Logistics Outbound Voice # How Ekart improved NDR recovery and lead qualification with Ringg Ekart partnered with Ringg to automate two high-volume workflows: non-delivery report (NDR) calling and business lead qualification. Ekart was able to improve follow-up speed, reduce manual effort, and make these workflows easier to scale. Published on: 09 Jul 2026 1 L+ Calls handled per month 75% Coverage across India 5+ Languages Supported ## Overview For Ekart, scale creates two very different but equally important operational challenges. The first is delivery recovery. When a shipment is not delivered, someone has to follow up with the customer, understand whether they still want the parcel, and determine whether a re-attempt should happen or whether the shipment should be marked as return to origin (RTO). At volume, that becomes an expensive and repetitive workflow. The second is business lead qualification. When new businesses sign up on Ekart, the initial qualification step still needs to happen quickly and consistently before the lead reaches the internal team. That was the problem Ekart wanted to solve, It needed a way to automate first-touch conversations across both operations, reduce the load on human teams, and improve outcomes without scaling headcount linearly. ![Ekart case study thumbnail](https://images.prismic.io/ringg-ai/xuhDKBeRgG0S8tO-_image-12-.png?auto=format%2Ccompress&fit=max&w=3840) ## Why non-delivery report (NDR) calling is hard to scale manually Both workflows depend on fast, repetitive communication. In the NDR flow, the challenge is straightforward but operationally heavy. When a shipment is not delivered, Ekart has to reach the customer, understand whether they are still interested in receiving it, and then decide whether to trigger a re-attempt or close the loop as RTO. If that follow-up is delayed or inconsistent, re-delivery opportunities are lost and return costs increase. In lead qualification, the challenge is different but similar in structure. New businesses signing up on Ekart need to be contacted, qualified, and then transferred to the right team. Without automation, that first layer of outreach consumes valuable human bandwidth and slows down response time. Ekart needed a way to handle both workflows quickly, consistently, and across languages. ## How Ekart used Ringg for RTO reduction and lead qualification Ekart used Ringg to deploy AI voice agents across both NDR calling and business lead qualification. That included: * AI-led NDR follow-up calls for undelivered shipments * Customer confirmation on whether a delivery re-attempt should happen * AI-led first-touch qualification for business signup leads * Handoff of qualified leads to Ekart’s internal team * Multilingual support across key customer-facing workflows SEE IT FOR YOUR TEAM We’ll show you a live agent reducing RTO through smarter NDR calling. In 15 minutes. [Book a walkthrough](https://www.ringg.ai/book-a-demo) ## Built to support multilingual delivery and qualification workflows The deployment was designed around the realities of high-volume logistics operations. For NDR calling, Ringg helped Ekart solve for customer follow-up at scale, especially in Bharat facing workflows where language support matters. The system was launched with Hindi as the primary language and later expanded to include Tamil, Telugu, and Kannada. For lead qualification, the workflow operated in Hindi and English and was built around a single-node agent setup. Across both use cases, Ringg was positioned as a scalable internal calling layer that Ekart could operationalise across teams. “Ringg has been a strong partner in our journey because they have been able to deliver a high degree of product personalisation for a company at our scale. They have also been highly effective at handling objections and bringing a more human-like touch to our calls.” ![Rishabh Iyer](https://images.prismic.io/ringg-ai/AmsNp-pqPVC0fkA6_RishabhIyer.jpeg?auto=format%2Ccompress&w=828&fit=crop) [Rishabh Iyer](https://www.linkedin.com/in/rishabh-iyer-bab73b227/) Product Manager, Ekart ## What changed across NDR recovery and lead qualification 1 L+ Calls handled per month 75% Coverage across India 5+ Languages supported ## Built for logistics workflows, recovery, and scale The details operations and support teams care about before rollout. Multilingual calling Hindi-first, with Tamil, Telugu, Kannada, and English across workflows High-volume operations Supports NDR and qualification calling at production scale Real-time routing Qualified business leads transferred to the right internal team Expansion-ready setup Foundation in place for adjacent workflows like non-pickup reason calls BUILD YOURS NEXT ## Your deliveries. Recovered before they turn into RTO. The Ekart deployment shows what AI-first logistics workflows look like at scale. Timely NDR calling, multilingual customer follow-up, and faster lead qualification across high-volume operations. In logistics, follow-up matters. With AI, follow-up finally scales. [See pricing](https://www.ringg.ai/pricing)[Book a demo](https://www.ringg.ai/book-a-demo) ## Other businesses transformed with Ringg [All case studies](https://www.ringg.ai/case-studies) [ ![Practo-logo](https://images.prismic.io/ringg-ai/agQIy6YofJOwHJ8o_practologo.png?auto=format%2Ccompress&fit=max&w=3840) Healthcare, Inbound voice ### How Practo Improved Patient Booking with Ringg Practo used Ringg to handle appointment booking at scale through AI voice agents that answered instantly, checked availability, and confirmed bookings in real time. 13 May 2026 ](https://www.ringg.ai/case-studies/practo)[ ![PolicyBazaar-logo](https://images.prismic.io/ringg-ai/agNHeaYofJOwHIpp_policybazaarheader.png?auto=format%2Ccompress&fit=max&w=3840) Insurance, outbound voice ### How PolicyBazaar Improved Lead Qualification at Scale with Ringg Policybazaar used Ringg to engage new leads instantly, qualify intent, and route the warmest prospects to human advisors at scale. 12 May 2026 ](https://www.ringg.ai/case-studies/policybazaar)[ ![PharmEasy-case-study](https://images.prismic.io/ringg-ai/agLeqqYofJOwHG66_PEheader.png?auto=format%2Ccompress&fit=max&w=3840) Healthcare, OUTBOUND VOICE ### How PharmEasy Improved Medicine Reorder Conversion with Ringg PharmEasy partnered with Ringg to improve medicine reorder conversion with timely AI voice reminders and WhatsApp follow up. The goal was simple: bring users back into the reorder journey exactly when they were most likely to need a refill. 12 May 2026 ](https://www.ringg.ai/case-studies/pharmeasy) Source: https://www.ringg.ai/case-studies/ekart