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Capabilities
Adapts responses in real time to user interruptions while retaining full conversational context, ensuring interactions remain natural, context-aware, and aligned with customer intent throughout the journey.
Guides users toward defined objectives by intelligently identifying and redirecting off-topic or out-of-scope queries, ensuring focused, efficient, and outcome-driven interactions.
Ensures real-time transfer of customer inquiries to human agents while preserving full context, delivering seamless, efficient, and consistent support experiences.
Builds customer personas to engage everyone at their preferred time, language, and channel, ensuring more personalized and effective interactions.
Enables on-the-fly language switching, allowing conversations to seamlessly change languages at any point for more inclusive and effective customer interactions.
Response-Based Workflow Switch enables accurate intent recognition to dynamically direct customer interactions, ensuring timely, relevant, and context-aware responses.
Provides Voice of the Customer (VoC) insights by analyzing sentiment and intent across customer interactions. These insights help enterprises boost conversions, reduce costs, and improve CX through, targeted agent coaching, and data-driven process optimization.
Provides Voice of the Customer (VoC) insights by analyzing sentiment and intent across customer interactions. These insights help enterprises boost conversions, reduce costs, and improve CX through, targeted agent coaching, and data-driven process optimization.
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Adapts responses in real time to user interruptions while retaining full conversational context, ensuring interactions remain natural, context-aware, and aligned with customer intent throughout the journey.
Ensures real-time transfer of customer inquiries to human agents while preserving full context, delivering seamless, efficient, and consistent support experiences.
Guides users toward defined objectives by intelligently identifying and redirecting off-topic or out-of-scope queries, ensuring focused, efficient, and outcome-driven interactions.
Response-Based Workflow Switch enables accurate intent recognition to dynamically direct customer interactions, ensuring timely, relevant, and context-aware responses.
Builds customer personas to engage everyone at their preferred time, language, and channel, ensuring more personalized and effective interactions..
Enables on-the-fly language switching, allowing conversations to seamlessly change languages at any point for more inclusive and effective customer interactions.
Adapts responses in real time to user interruptions while retaining full conversational context, ensuring interactions remain natural, context-aware, and aligned with customer intent throughout the journey.
Ensures real-time transfer of customer inquiries to human agents while preserving full context, delivering seamless, efficient, and consistent support experiences.
Guides users toward defined objectives by intelligently identifying and redirecting off-topic or out-of-scope queries, ensuring focused, efficient, and outcome-driven interactions.
Response-Based Workflow Switch enables accurate intent recognition to dynamically direct customer interactions, ensuring timely, relevant, and context-aware responses.
Builds customer personas to engage everyone at their preferred time, language, and channel, ensuring more personalized and effective interactions.
Enables on-the-fly language switching, allowing conversations to seamlessly change languages at any point for more inclusive and effective customer interactions.
Real results. Proven impact. Outcomes that speak for themselves, delivering measurable value across enterprise operations and customer engagement.
Maruti's service centers were retaining customers for only 1.5 years compared to the industry average of 11 years, resulting in significant revenue leakage in a high-margin vertical.
Rezo's Agentic AI platform enabled self-service for simple queries, automated service scheduling, outreach to dormant customers, timely reminders, post-service feedback collection and upgradation calls.
With only a small fraction of calls being analyzed manually, Spinny had limited visibility into customer experience and agent performance, leading to lower conversions.
Rezo's Agentic AI solution monitored customer–agent interactions, providing automated customer-reason categorization, detailed agent performance evaluation, sentiment and lead-score analysis.
Livguard was operating two separate contact centers with limited availability, creating inefficiencies in managing inbound queries and service requests that impacted operations and customer experience.
Rezo's Agentic AI system autonomously handled inbound queries, streamlined complaint registration, tracked complaint status, delivered product information and identified upsell opportunities.
With low connect rates, limited follow-ups, and a capacity of only 400 calls per day, IIT Delhi was struggling to meet its goal of enrolling 200 students in a new course within 20 days.
Rezo's Agentic AI Agents enabled personalized outreach with automated student categorization, self-managed call-volume optimization, continuous self-improvement and regular follow-ups.
Overwhelming claim-status queries, manual paperwork, and error-prone operations were causing delays for a leading insurance provider in resolving claims, ultimately impacting customer satisfaction.
Rezo's Agentic AI platform can accurately fetch claim numbers from the database, enabling autonomous information handling, personalized resolutions and reduced agent workload.
A leading Indian NBFC was struggling with low collection efficiency and needed a more personalized outreach approach to help customers make timely payments.
Rezo's Agentic AI agents analyzed existing customer data and delivered personalized interactions by selecting the optimal channels, language and timing, while conducting human-like conversations.
Maruti's service centers were retaining customers for only 1.5 years compared to the industry average of 11 years, resulting in significant revenue leakage in a high-margin vertical.
Rezo's Agentic AI platform enabled self-service for simple queries, automated service scheduling, outreach to dormant customers, timely reminders, post-service feedback collection and upgradation calls.
With only a small fraction of calls being analyzed manually, Spinny had limited visibility into customer experience and agent performance, leading to lower conversions and missed opportunities.
Rezo's Agentic AI solution monitored customeragent interactions, providing automated customer-reason categorization, detailed agent performance evaluation, sentiment and lead-score analysis.
Livguard was operating two separate contact centers with limited availability, creating inefficiencies in managing inbound queries and service requests that impacted operations and customer experience.
Rezo's Agentic AI system autonomously handled inbound queries, streamlined complaint registration, tracked complaint status, delivered product information and identified upsell opportunities.
With low connect rates, limited follow-ups, and a capacity of only 400 calls per day, IIT Delhi was struggling to meet its goal of enrolling 200 students in a new course within 20 days.
Rezo's Agentic AI Agents enabled personalized outreach with automated student categorization, self-managed call-volume optimization, continuous self-improvement and regular follow-ups.
Overwhelming claim-status queries, manual paperwork, and error-prone operations were causing delays for a leading insurance provider in resolving claims, ultimately impacting customer satisfaction.
Rezo's Agentic AI platform can accurately fetch claim numbers from the database, enabling autonomous information handling, dynamic process navigation, personalized resolutions and reduced agent workload.
A leading Indian NBFC was struggling with low collection efficiency and needed a more personalized outreach approach to help customers make timely payments.
Rezo's Agentic AI agents analyzed existing customer data and delivered personalized interactions by selecting the optimal channels, language and timing, while conducting human-like conversations.

