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Projects & Prototyps

Work Experience

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Projects & Prototyps

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Scoot - Find Concerts, Meetups & More on WhatsApp

Scoot is a WhatsApp-based event discovery assistant that helps users find local concerts, meetups, and open mics based on their interests and location. Weekend planning was often disrupted by context-switching between chat apps and event platforms, leading to missed opportunities and low spontaneity. Our goal was to create a seamless, chat-native experience that delivers personalized event recommendations directly within WhatsApp—eliminating friction, enhancing real-time coordination, and making social planning effortless and fun.

Ownership

Ownership

End-to-end (Product, UX, Engineering)

End-to-end
(Product, UX, Engineering)

Platform

Platform

WhatsApp (Twilio Business API)

Stack

Stack

Node.js, Express, Ticketmaster API,
OpenAI GPT-4o (intent recognition)

Node.js, Express
Ticketmaster API
OpenAI GPT-4o
(intent recognition)

Prototype

Prototype

Fully functional, API-integrated chatbot

Fully functionalAPI-integrated chatbot

Launch Date

Launch Date

May 2025

Github

https://github.com
sumanthns96/Scoot

https://github.com/
sumanthns96/Scoot

Problem Statement

Event discovery remains fragmented and inefficient despite the digital transformation of ticketing platforms. Current challenges include:

Discovery Fragmentation: Users must navigate multiple platforms, websites, and social media channels to find relevant events, creating decision fatigue and missed opportunities. The existing ecosystem requires users to actively seek out events rather than receiving personalized, location-based recommendations through their preferred communication channels.

Information Asymmetry: While 74% of potential attendees research events before purchasing, the information gathering process is time-intensive and often incomplete. Users struggle to access comprehensive event details, pricing comparisons, and availability status in real-time conversations.

The WhatsApp chatbot ecosystem provides the technological foundation for this opportunity. The market is experiencing explosive growth from $1.34 billion in 2023 to a projected $10.29 billion by 2030. Key performance indicators demonstrate the platform's effectiveness: 70% of businesses using WhatsApp chatbots reported 25% increased customer engagement within the first year, while 80% of users prefer interacting with businesses through WhatsApp chatbots, citing convenience and speed as primary motivators.

WhatsApp Business has achieved remarkable scale with over 966.5 million monthly active users and more than 50 million businesses utilizing the platform globally. The platform's superior engagement metrics include a 98% message open rate compared to traditional email marketing, conversion rates of 45-60% for marketing campaigns, and the ability to recover 45-60% of abandoned cart scenarios through automated messaging

Solution Positioning and Value Proposition

The WhatsApp Event Discovery Bot positions itself as an intelligent event concierge that transforms passive event discovery into an active, conversational experience. By integrating Ticketmaster's comprehensive event database with WhatsApp's communication infrastructure, the solution delivers personalized, location-aware event recommendations directly within users' primary messaging environment.

The initial Ticketmaster integration provides access to a vast event catalog spanning concerts, sports, theater, and entertainment. The planned expansion to Eventbrite and Meetup platforms will broaden the scope to include professional conferences, community gatherings, workshops, and networking events, creating a comprehensive event discovery ecosystem.

Competitive Differentiation

The solution differentiates through conversational commerce capabilities that leverage WhatsApp's superior engagement metrics. Unlike traditional event discovery applications that require separate downloads and account creation, this bot integrates seamlessly into users' existing communication workflows. The 98% message open rate and 45-60% conversion rates demonstrated by WhatsApp business messaging provide significant advantages over email-based or app-based discovery methods.

Location-based intelligence combined with natural language processing enables sophisticated filtering and recommendation algorithms. Users can specify preferences through conversational interactions rather than navigating complex filter interfaces, reducing friction and improving user experience quality

35%

Improved onboarding process

25%

Increase in user retention

84%

Increase in time spent on website

Process

1. Product Research & Problem Definition

I started by identifying a real user pain point: people struggle to find local events easily and end up juggling multiple apps or sites. I talked to friends, colleagues, and potential users—music fans, young professionals, parents—to confirm this frustration. I also researched the market size for event ticketing and the growing use of WhatsApp chatbots for business and consumer needs. This helped me see a clear gap and opportunity.

2. Opportunity Validation

To validate the idea, I ran a simple survey and a “fake door” test—posting on social media as if the bot already existed to see how many people clicked or asked for access. I used the Lean Value Tree to map out how the product would support business goals and user needs, and did some quick competitor analysis to spot what was missing in existing solutions.

3. Feature Scoping & Prioritization

I defined the core features:

  • Location-based event discovery

  • Ticketmaster API integration (with plans for Eventbrite and Meetup later)

  • Conversational interface on WhatsApp

  • Must-haves: Basic category search, location detection, natural language prompting

  • Performance boosters: Personalized recommendations, quick ticket info

  • Delighters: Group planning, calendar sync (planned for later)

I sketched out user flows and dialogue trees to visualize how users would interact with the bot.

4. Rapid Prototyping & Vibe Coding

I used tools like Lovable and Claude to design the conversation logic. I then built the backend using Cursor, integrating the Ticketmaster API myself. I set up a GitHub repo for version control and to keep track of my progress. Since I was working alone, I managed the entire codebase and kept development fast and focused.

5. Infrastructure & Deployment

I chose Railway.app for hosting—no need for a big cloud platform like AWS. Railway made it easy to deploy and scale as needed. I set up basic monitoring and analytics myself to track user engagement and bot performance from day one.

6. Beta Testing & Launch

I launched the bot to a small group of beta testers—friends, family, and early adopters. I collected feedback, fixed bugs, and iterated on the user experience myself. Once I was confident, I rolled out to a broader audience, monitoring key metrics like user acquisition, engagement, and conversion rates

1. Product Research & Problem Definition

I started by identifying a real user pain point: people struggle to find local events easily and end up juggling multiple apps or sites. I talked to friends, colleagues, and potential users—music fans, young professionals, parents—to confirm this frustration. I also researched the market size for event ticketing and the growing use of WhatsApp chatbots for business and consumer needs. This helped me see a clear gap and opportunity.

2. Opportunity Validation

To validate the idea, I ran a simple survey and a “fake door” test—posting on social media as if the bot already existed to see how many people clicked or asked for access. I used the Lean Value Tree to map out how the product would support business goals and user needs, and did some quick competitor analysis to spot what was missing in existing solutions.

3. Feature Scoping & Prioritization

I defined the core features as follows:

  • Location-based event discovery

  • Ticketmaster API integration (with plans for Eventbrite and Meetup later)

  • Conversational interface on WhatsApp

  • Must-haves: Basic category search, location detection, natural language prompting

  • Performance boosters: Personalized recommendations, quick ticket info

  • Delighters: Group planning, calendar sync (planned for later)

I sketched out user flows and dialogue trees to visualize how users would interact with the bot.

4. Rapid Prototyping & Vibe Coding

I used tools like Lovable and Claude to design the conversation logic. I then built the backend using Cursor, integrating the Ticketmaster API myself. I set up a GitHub repo for version control and to keep track of my progress. Since I was working alone, I managed the entire codebase and kept development fast and focused.

5. Infrastructure & Deployment

I chose Railway.app for hosting—no need for a big cloud platform like AWS. Railway made it easy to deploy and scale as needed. I set up basic monitoring and analytics myself to track user engagement and bot performance from day one.

6. Beta Testing & Launch

I launched the bot to a small group of beta testers—friends, family, and early adopters. I collected feedback, fixed bugs, and iterated on the user experience myself. Once I was confident, I rolled out to a broader audience, monitoring key metrics like user acquisition, engagement, and conversion rates

Designed by Sumanth Using Framer

LinkedIn

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Designed by Sumanth Using Framer

LinkedIn

Mail

Designed by Sumanth Using Framer

LinkedIn

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