--- title: "PolicyBazaar: improving lead qualification at scale with Ringg" description: "See how PolicyBazaar used Ringg to contact 57,000 leads daily, qualify intent in under 60 seconds, and route the warmest insurance prospects to human advisors." canonical_url: "https://www.ringg.ai/case-studies/policybazaar" last_updated: "2026-06-01T10:53:24.000Z" --- Announcing our $15 Million Series A led by Peak XV [Read more](https://techcrunch.com/2026/08/25/indias-ringg-gets-backing-from-peak-xv-as-it-pushes-voice-ai-past-the-phone-call/) 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. Published on: 12 May 2026 57,000+ Connected calls daily 4 min Average qualified call time 5 - 7% Qualified lead rate 67% L0 containment rate ## Overview At Policybazaar, every day, thousands of users come to the platform looking for a term plan, health cover, motor policy, or another insurance product. The challenge is not generating demand. The challenge is reaching that demand while it is still warm. That was the problem Policybazaar wanted to solve. It needed a better way to respond to the leads it was already getting, quickly, consistently, and at scale. ![PolicyBazaar-case-study](https://images.prismic.io/ringg-ai/agNHdqYofJOwHIpn_Policybazaarcase-study.png?auto=format%2Ccompress&fit=max&w=3840) ## Why insurance lead intent cools quickly When someone fills out a form for a term plan or health policy, the intent is often real. They may be thinking about family, financial protection, risk, cost, or timing, but that intent does not stay warm forever. In the earlier process, a lead came in, was assigned to a relationship manager, and got called when someone was available. Sometimes that happened quickly. Sometimes it did not. At Policybazaar’s scale, even a strong human team could not guarantee that every new lead would be reached in the first minute. By the time a customer receives a call, they may have already moved on, spoken to another provider, ignored the number, or lost the urgency that made them fill out the form in the first place. Policybazaar needed a way to reach every lead immediately, qualify the serious ones properly, and make sure human advisors were spending their time where it mattered most. BEFORE RINGG Average lead response time 8 - 12 min Intent fades fast after a form fill RM call capacity per day ~80 calls A drop in the ocean compared to daily inbound volume Qualified lead rate 5 - 7% No proper qualification step before a human got involved. ## How Policybazaar built a real time qualification layer with Ringg Policybazaar used Ringg to create an outbound voice AI layer on top of its existing telephony infrastructure. When a new lead came in, Ringg triggered an outbound call in under 60 seconds. The system was designed to speak naturally, understand customer intent, qualify the prospect, and route the right leads to the right human relationship managers. That included: * Immediate outbound calls after form submission * Qualification across intent, profile, budget, and coverage needs * Multilingual conversations in Hindi, English, and Hinglish * Intelligent routing of the warmest leads to human RMs with context See it for your team We'll show you a live agent calling your leads. In 15 minutes. [Book a walkthrough](https://www.ringg.ai/book-a-demo) ## Why the model worked at Policybazaar’s scale A strong first call does not always need to be long. It needs to be timely, relevant, and structured, that is what made this system effective. In roughly four minutes, Ringg could confirm interest, understand the customer profile, capture budget comfort, identify coverage expectations, and assess urgency. For many leads, that first AI-led conversation was enough to resolve the initial layer of qualification without needing immediate human involvement. Instead of relying on a human RM to perform the same repetitive first filter across thousands of leads, Policybazaar was able to respond in real time and gather structured information at the start of the journey. That changed the role of the RM completely, they arrived with context: product interest, budget, coverage requirement, and preferred callback timing. That meant relationship managers could begin from a much more informed place and spend more of their time on people who were actually ready to move forward. "Before, our RMs were spending half their day on people who'd filled the form out of curiosity. Now, by the time a lead lands in front of them, the AI has already had the hard first conversation. Our close rates on RM calls are up significantly. Every lead they see is genuinely warm." ![Manoj Image](https://images.prismic.io/ringg-ai/ah1ixgeQX7-eWexK_manoj.jpeg?auto=format%2Ccompress&w=1920&fit=crop) [Manoj Pandey](https://www.linkedin.com/in/manoj-pandey1992/) DVP, AI Initiatives & Health, Policybazaar ### What happens after the first call 67% of calls are fully handled by AI. Only the best 7% reach an RM, already briefed with the full call context. The remaining 26% get a scheduled callback. Fully handled by AI 67% FAQs, eligibility, soft closure. No human needed. RM handoff 7% High intent, right profile. Transferred with full call context. Callback scheduled 26% Interested but unavailable right now. Booked back into the queue. ## What changed across response time and lead quality 57,000+ Every inbound lead reached, at consistent quality, every day 4 min Long enough to find intent, budget, and cover need before the RM picks up 5 - 7% High-intent leads surfaced and handed to human RMs, ready to close 67% Leads handled fully by AI, with no human cost at all What 4 minutes covers The kind of qualification a human RM would take 15 to 20 minutes to reach. Done on the first call. 0 to 60s: Intent confirmation Product interest, coverage type, reason for enquiry 1 to 2 min: Profile qualification Age, income band, existing policies, health status basics 2 to 3 min: Budget & cover sizing Sum assured range, premium comfort, policy term preference 3 to 4 min: Nominee & urgency Nominee readiness, timeline to buy, preferred callback window ## Built for multilingual, on-premise insurance operations The deployment was designed to work within Policybazaar’s existing operating environment. Ringg was implemented using Policybazaar’s current telephony infrastructure through a BYOT model. It supported multilingual conversations across Hindi, English, and Hinglish, and was built with enterprise-grade security requirements in mind. Scale architecture Built for 57K calls a day, with headroom Speed to lead pipeline From form fill to live call in under 60 seconds Qualification framework Dynamic L0, L1, and L2 routing logic Bring Your Own Telephony (BYOT) Ringg runs on Policybazaar's own carrier infrastructure Knowledge Base, 200 policy documents, multilingual Term, health, motor, and investment products in Hindi and English On-premise data, models trained on it Call data stays inside Policybazaar's environment Insurance grade by design * TRAI DND Filtered * IRDA Disclosure compliant * ISO 27001 * SOC 2 Type II * India Data Residency Build yours next ## Your leads. Called in seconds. The Policybazaar deployment is what AI-first lead operations looks like at scale. Every lead called within a minute. Every conversation qualified properly. Every RM's time spent where it actually creates value. In insurance, speed wins. With AI, speed is finally free. [Book a demo](https://www.ringg.ai/book-a-demo)[See pricing](https://www.ringg.ai/pricing) ## Other businesses transformed with Ringg [All case studies](https://www.ringg.ai/case-studies) [ ![PlatinumRx logo](https://images.prismic.io/ringg-ai/ah0fVAeQX7-eWd4O_1475.png?auto=format%2Ccompress&fit=max&w=3840) Healthcare, Inbound Voice ### How PlatinumRx Improved Inbound Healthcare Support at Scale with Ringg PlatinumRx partnered with Ringg to improve inbound support at scale through AI voice agents that could understand customer needs, retrieve relevant order information, and route conversations to the right team in real time. 01 Jun 2026 ](https://www.ringg.ai/case-studies/platinumrx)[ ![smallcase-case-study](https://images.prismic.io/ringg-ai/agL7YqYofJOwHHYw_smallcaseheader.png?auto=format%2Ccompress&fit=max&w=3840) Fintech, OUTBOUND VOICE ### How smallcase Improved Investor Conversion with Ringg smallcase partnered with Ringg to improve investor conversion across onboarding, product adoption, and subscriptions. By adding real-time, contextual outreach at key moments, it helped turn existing user intent into action. 12 May 2026 ](https://www.ringg.ai/case-studies/smallcase)[ ![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) Source: https://www.ringg.ai/case-studies/policybazaar