Cars24 now handles over a million conversation minutes a month with AI-powered agents, recovering 12% of previously lost seller leads and boosting customer support resolution rates by 50%. The numbers show how AI can scale high-touch support operations without continuously expanding headcount, even in an industry where most sales still depend on calls, documents, and days-long follow-ups.
Car buying and selling in India rarely happens in a single session. Customers compare options over calls, book inspections, arrange financing, and often drop out if follow-ups stall. For Cars24, which runs one of the world's largest AI-native automotive ecosystems across India, the UAE, and Australia, this created a core operational tension: how to deliver consistent, high-quality experiences across millions of interactions without adding more agents for every new lead.
The company's answer was to deploy voice and chat agents built on OpenAI's technology across the full customer lifecycle - from initial inquiry to post-purchase support. The agents now handle over a million conversation minutes a month, and the team recorded a 50% increase in customer support resolution rates. That result makes clear what AI for Customer Support can achieve when it's tied directly to the workflows that drive conversion and retention.
How the agents work across the customer lifecycle
When a buyer calls, an AI agent asks about budget, family size, commute needs, and preferred car type, then recommends vehicles from the Cars24 catalog. It books a test drive, helps explore financing options, and follows up before the visit to confirm or suggest alternatives. After the test drive, the agent checks whether the customer wants to proceed, book another visit, or explore a different car. Post-purchase, the same system handles feedback, warranty questions, returns, and after-sales service.
For sellers, the workflow follows a similar path: an AI agent collects vehicle details, schedules an inspection, sends reminders, and reschedules missed appointments. Crucially, for leads that used to go cold after 10 days, AI agents now re-engage customers, qualify renewed intent, and return them to the funnel - a change that recovered 12% of previously lost seller leads. This end-to-end approach is a real-world example of AI Agents & Automation managing multi-step interactions that once demanded dedicated human teams.
Internal adoption spreads beyond the contact center
Cars24 didn't stop at customer-facing roles. The company rolled out ChatGPT Enterprise and Codex to about 600 employees across engineering, finance, legal, marketing, and operations, with 85% to 90% daily active usage. Product managers use Codex to create and refine tasks, engineering teams tag it into bug reports, and it summarizes work across GitHub, reducing the number of standups needed to keep projects moving.
"I thought Codex would make our engineers faster. What surprised me was how quickly it spread beyond engineering," said Jayesh Gupta, Builder-AI & Innovation at Cars24. "Product managers, finance teams, and even day-to-day workflows started changing. That is when I realised we had not just changed how we write code but had changed how the entire company thinks about getting work done."
Finance teams now use Codex to pull numbers from systems of record, run analysis, and prepare investor reports without chasing inputs from multiple business heads. Another workflow automatically reviews purchase requests above a threshold, flags anomalies, and auto-approves clean requests. Some teams have built "chief of staff" agents that connect Slack, Gmail, and WhatsApp to manage communication, scheduling, and hiring workflows.
Why this matters for customer support
Cars24's experience shows that AI agents can handle the kind of high-volume, multi-step conversations that often overwhelm support queues. Starting with the most constrained part of the funnel - the middle and bottom, where conversation drives conversion - let the team prove value fast. The 50% resolution rate improvement and 12% lead recovery are not theoretical; they came from tying AI to specific, measurable workflows.
For support leaders, the takeaway is practical: identify the repeatable, high-effort interactions that cause drop-off, then deploy AI agents to manage them end-to-end. Cars24 also demonstrates that once AI proves itself in customer-facing roles, it often spreads internally, helping teams automate reporting, scheduling, and follow-ups without waiting for centralized engineering support. As Vikram Chopra, Builder at Cars24, said: "Buying a car in India is a journey, not a transaction. For years, the experience depended on who picked up the phone. AI changes that. Today, we handle over a million conversation minutes a month through AI, giving every customer a high-quality experience at any scale."
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