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Voco

A developer built Voco, a voice-first event RSVP engine that converts uploaded CSV rosters into personalized AI-generated voice reminder calls to reduce RSVP-to-attendance drop-off. The system pairs a React/Vite dashboard with a Node.js/Express backend on Render that uses an open-weight Llama-3 model to write two-sentence reminder scripts, ElevenLabs for voice synthesis, MongoDB Atlas for persistence, and Sentry for pipeline tracing and latency monitoring. The developer says a local tech community organizer reported higher turnout after using the personalized voice clips in place of standard email reminders.

by read3 min views1 publishedOct 18, 2026

What I Built

I built Voco, a Voice-First Event RSVP Engine designed to solve one of the biggest headaches for community organizers and field operations teams: RSVP-to-attendance drop-off.

Event organizers, tech communities (like GDG or Campus Spark), and maker labs often suffer from low turnout despite high registration numbers because standard text or email reminders get ignored. Voco solves this by allowing organizers to upload their RSVP lists and automatically generate highly personalized, human-sounding voice reminder calls or audio drops for each attendee.

Because it actually gets people in the door, local event organizers and niche community leads would gladly pay a small per-event fee ($10-$20) or a monthly SaaS subscription to use it. Code

A minimalist, Voice-First Event RSVP Engine that converts standard RSVP lists into personalized, AI-generated voice reminders to increase event attendance.

/voco: Frontend application built with React and Vite. Provides a minimalist light-mode dashboard for CSV roster uploads, campaign tracking, and audio preview playback./api: Backend service built with Node.js and Express. Handles CSV ingestion, MongoDB Atlas persistence, Llama-3 script generation, and ElevenLabs voice synthesis. How I Built It

Voco is designed to be incredibly lean and professional. The UI is a strict, light-mode functional dashboard built with React, Vite, TypeScript, and pnpm, heavily optimized for speed and utility.

The backend runs on Render, orchestrating the AI workflow:

Data Layer: MongoDB Atlas securely stores event details and attendee states.

Open-Source AI Core: I utilized an open-weight model (like Llama-3) to parse the attendee data (Name, Role, Event) and dynamically write a personalized, 2-sentence conversational reminder script.

Voice Synthesis: ElevenLabs takes the open-weight model's text and synthesizes a warm, energetic voice.

Observability: Sentry is integrated into the Node.js backend to trace the pipeline, monitor latency, and catch any inference timeouts when hitting the AI APIs.

Why Does Open Innovation Matter?

For a side hustle like Voco, open innovation is the only way the unit economics and privacy guarantees actually work. Protecting Margins: By relying on open-weight models instead of closed, general-purpose APIs, the per-message generation cost remains near zero. This ensures the side hustle remains profitable even if scaled to hundreds of events.

Data Privacy: Organizers are trusting Voco with their communities' phone numbers, names, and job titles. Utilizing open-source AI ensures that attendee data isn't being fed into a proprietary black box to train someone else's model. The data stays strictly within the architecture we control.

My Agent Session

My First User

I tested Voco with a local tech community event. The organizer loved how the personalized voice clips cut through the noise of standard email reminders, and attendees felt a much stronger personal connection to the event, resulting in a higher turnout rate.

Prize Categories

I am submitting Voco for the following partner prize categories:

Best Use of Render: The Node.js backend and AI orchestration pipeline are hosted securely on Render.

Best Use of ElevenLabs: Used to generate the final, human-sounding voice narration from the AI-generated scripts.

Best Use of MongoDB Atlas: Used as the core database to store RSVP lists, event details, and campaign statuses.

Best Use of Sentry Agent Tracing: Implemented to monitor backend performance, trace the AI generation pipeline, and debug latency issues.

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