Building a Voice AI Agent That Turns User Drop-Offs Into Conversations

Our team, Rho secured 2nd place at a Voice AI Buildathon by building an AI voice agent for user reactivation and referrals.

The most exciting part? We didn't just present a working prototype. We demonstrated it through a real outbound call, with Bachatt co-founders Mayank Agarwal and Ankur Jhavery, along with judges from Urban Company and OLX, interacting with our agent.

Here's what we built, the problem we wanted to solve, and how we approached it.

Imagine a user downloads a savings app, starts onboarding, and abandons the process halfway through.

Your analytics dashboard can tell you where the user dropped off. It might show that they never completed KYC, linked their bank account, or made their first investment.

But it can't necessarily tell you what happened on the other side of the screen.

Traditional re-engagement often relies on push notifications, emails, and automated reminders. These can bring users back, but they don't always help identify the underlying friction.

We wanted to explore a different approach.

What if, instead of sending another reminder, the product could actually call the user, understand their concern, and help them take the next step?

That became the core idea behind our project.

We built a voice agent around Bachatt's reactivation and referral journey. The broader concept, however, applies to any product where users encounter friction during onboarding or fail to complete an important action.

When a user stalls during a critical onboarding step, the system can identify the point of friction and initiate a relevant interaction.

Rather than delivering a generic message, the agent aims to understand the user's specific concern and provide contextual guidance.

For example, a user who hasn't completed a step should receive help relevant to that step, rather than a generic reminder to finish onboarding.

Referral links bring prospective users into a product, but downloading an app doesn't guarantee that they will complete registration or become active users.

Our second scenario focuses on referred users who don't progress after receiving a WhatsApp referral link.

The proposed flow gives the user time to download and onboard. If they haven't progressed after a minimum 36-hour wait, the agent can call them, answer questions, help resolve minor issues, and guide them towards the next step.

The objective is not simply to get someone on a call. It's to remove the friction preventing them from moving forward.

The central design principle was context-aware interaction.

A voice agent needs more than a language model and a phone connection to be useful in a real product workflow. It needs to know where the user is in their journey and what action makes sense next.

Our proposed system follows this high-level flow:

The system uses Model Context Protocol (MCP) to query live journey-stage information. This helps ground the interaction in the user's current state rather than relying on a generic script.

The onboarding journey can include stages such as authentication, KYC, profile setup, bank account linking, plan selection, and the first savings action.

The guiding principle is simple: help the user with the next relevant step, not the entire journey at once.

A conversational agent can sound natural and still be ineffective if it doesn't understand what the user needs.

Consider two users who haven't completed onboarding.

One might be stuck because they don't understand a particular step. The other might have encountered a technical issue.

Sending both users the same message ignores the difference between their situations.

By connecting the agent to the product's journey state, we explored how voice interactions could become more targeted and useful.

There is also an important boundary here, particularly in financial products. Our design keeps the agent within the scope of platform-related assistance rather than providing broad financial advice.

The goal is to help users navigate the product, not make decisions on their behalf.

One of the highlights of the buildathon was demonstrating the system through a real outbound call.

Bachatt co-founders Mayank Agarwal and Ankur Jhavery interacted with our agent, as did Parth Mahajan from Urban Company and Ajay Singh Rathore from OLX.

Getting to see people interact with the agent in real time made the experience very different from presenting an architecture diagram or a recorded demo.

It also opened up questions about what makes a voice agent genuinely useful: how it handles real conversations, how much context it needs, and how to ensure that its responses remain relevant to the user's situation.

We walked away with 2nd place, but the bigger takeaway was seeing the idea move from a concept to a working, demonstrable interaction.

This project reinforced a distinction that I think will matter increasingly as AI agents become part of everyday products.

There's a difference between an AI system that can hold a conversation and one that can take meaningful action within a product workflow.

The latter requires more than conversational ability. It needs context, access to the right information, clear boundaries, and a reason to initiate the interaction in the first place.

For me, the most interesting possibility is the feedback loop. Voice AI could potentially do more than help individual users complete a task. By surfacing recurring questions and points of confusion, it could help product teams improve the underlying experience for everyone.

That turns reactivation from a purely growth-oriented activity into a potential source of product insight.

Voice is one of the earliest ways humans learn to interact with the world, long before we learn to write. It is an incredibly natural bridge between minds.

As we build increasingly capable AI systems, voice offers an opportunity to make technology feel less like a sequence of interfaces and more like an interaction.

We're only beginning to explore what that looks like in real products.

And congratulations to everyone who participated!