Razorpay Deploys ElevenLabs Voice Agents to Scale Merchant Engagement in Hinglish.

Razorpay, one of India's largest payment platforms serving millions of merchants, is now using ElevenLabs voice agents to automate outbound merchant engagement at scale. The company deployed ElevenAgents to handle tasks that previously required manual outreach, including recovering churned accounts, driving feature adoption, and communicating during service incidents.

The voice agents operate in Hinglish, a blend of Hindi and English commonly used in Indian business communication, and capture structured insights from every conversation. According to ElevenLabs, the deployment transforms what was once a manual, inconsistent process into a repeatable workflow that can reach merchants at the right moment.

For a platform like Razorpay, timing is everything. Merchants need to be contacted when payment issues arise, when new features launch, or when an account shows signs of churning. Manual outreach doesn't scale well at that volume, and email often gets ignored.

Voice agents solve this by making proactive calls that feel conversational while collecting data from each interaction. The structured insights allow Razorpay to understand merchant concerns, track common issues, and adjust its approach based on real feedback.

The use of Hinglish is particularly significant. Many Indian merchants are more comfortable switching between Hindi and English than using either language exclusively. A voice agent that handles this code-switching naturally can improve engagement and reduce friction compared to English-only support.

ElevenAgents is ElevenLabs' conversational AI platform, designed for enterprises that need to automate customer interactions at scale. The system handles inbound and outbound calls, processes natural language, and integrates with existing workflows.

Key capabilities include:

• Multilingual support including code-switching between languages
• Structured data capture from unstructured conversations
• Integration with CRM and support tools to log insights automatically
• Customizable conversation flows tailored to specific use cases

The platform has been deployed by other major companies recently, including Klarna, which reduced time to resolution by 10X, and Revolut, which selected ElevenAgents to bolster customer support. Deutsche Telekom also announced a partnership with ElevenLabs earlier this year.

ElevenLabs recently announced that its voice AI can now be deployed on-premise and on-device, addressing enterprise concerns around data privacy and latency. This flexibility is crucial for regulated industries like financial services, where customer data often cannot leave specific infrastructure.

Razorpay's deployment reflects a broader shift among payment platforms toward conversational AI. As competition intensifies and merchant expectations rise, companies are looking for ways to provide personalized support at scale without proportionally scaling headcount.

Voice agents are particularly well-suited for payments because many merchant issues require quick resolution. A missed payment notification or a misunderstood fee structure can lead to churn. Automated voice outreach can address these issues proactively, before they escalate.

The structured insights captured by ElevenAgents also give Razorpay a feedback loop that manual calls rarely provide. Every conversation becomes data that can inform product decisions, support training, and operational improvements.

ElevenLabs points to a full case study for more details on the Razorpay deployment, though specific metrics on call volume, resolution rates, or merchant satisfaction were not disclosed in the announcement. The company continues to expand its enterprise customer base, with a recent $500 million Series D at an $11 billion valuation signaling strong investor confidence in the voice AI market.

For payment platforms and fintech companies, the Razorpay deployment offers a template for scaling merchant engagement without sacrificing quality or consistency. As voice AI becomes more capable of handling complex, multilingual conversations, more companies are likely to follow this approach.