Welp: Reverse Yelp

Dashboard
Welp to Me ElevenLabs Voice Agent
Review Page
Customer Lookup
Reddit Forum

Inspiration

Welp was born out of the most relatable frustrations for service workers: rude customers. One of our teammates, Nidhi, spent some time juggling rude customers behind the counter and she never had a safe outlet to rate or rant about them. By the same token, she never had a good way to show gratitude for kind, wholesome customers. We wanted to flip the script on platforms like Yelp: if customers can publicly grade restaurants, why shouldn’t waiters and other service workers be able to rate their customers for everyone to see? This conversation about a “Reverse Yelp” inspired us to build this exact platform. Thus, we created Welp. Check it out at: (https://welp.social)

What it does

Welp is a reverse review platform that allows restaurants and other businesses to rate and review their customers. Users rate customers on how they behave, tip, and treat employees. And with a press of a button, users can automatically post their review to our dedicated subreddit, r/Welping, with a catchy AI-generated title. r/Welping is the place to see the most funny, wholesome, or crazy customer service stories. We also have voice transcription for easy reviewing. We also built a unique feature called “Welp to Me!”, where after a particularly intense customer service interaction, users can vent their frustrations to an AI companion powered by ElevenLabs’ conversational AI. The AI companion loves to instigate and be sarcastic, so your Welping session will be fun, intense, and therapeutic at the same time.

How we built it

We built Welp using modern web technologies: Frontend: Next.js 15 with React 19, TypeScript, and Tailwind CSS for responsive UI. We used 21st.dev templates Backend: Supabase for database and auth, Reddit integration with OpenAI for post title generation. Voice Processing: Web Speech API for voice-to-text transcription during reviews State Management: React hooks with API integration for real-time updates Deployment: Configured for easy deployment with environment-based configuration using Netlify: https://cerulean-semifreddo-75d0bb.netlify.app/ and Entri custom domain AI Integration: Integrated ElevenLabs' Conversational AI Widget for real-time AI interactions. We spent a lot of time improving the system prompt to hit the perfect balance of sarcasm and empathy. The conversational AI widget was very useful and integrated with few issues into our platform since we didn’t have to build out TTS and STT individually. Implemented a custom "Steam Level" feature that visualizes user frustration levels during conversations

Challenges we ran into

Bolt’s code generation got us 90% of the way there, but we had to dive in and hand-tune the output to fit our Next.js routes, Supabase schemas, RLS policies, and real-time listener logic. We had to make sure that state was managed accurately on the client side to reflect data changes from a bunch of disparate sources (ElevenLabs, Reddit, Supabase). Also, integrating ElevenLabs for live “Welping” audio meant building retry and error-handling logic around network hiccups.

Beyond technical hurdles, we had to weigh privacy versus accuracy: enabling service workers to identify repeat customers without exposing personal data. We let users attach a name and phone number to a review (hashed and securely stored in Supabase), anonymize everything we post to Reddit, and internally aggregate hashed identifiers so we can accurately track customer ratings without risking doxxing or harassment. It took a lot of time figuring out these issues but we spent time looking at documentation and online sources to understand the issues.

Accomplishments that we're proud of

We launched a fully functional MVP with real-time reviews, a clean UI, and secure auth. We also successfully integrated a bunch of external tools with Supabase, ElevenLabs, and Reddit. We got pretty great at prompting Bolt, and found that with clear, well-architected prompts, Bolt could easily build out the features that we wanted. We also maintained high code quality with lean, type-safe modules, and took a privacy-first approach by developing a secure customer identification system that is accurate yet protects everyone’s data.

We also are proud of our creativity with this hackathon. Conceiving the reverse-Yelp model to help combat rude customers and celebrate good ones was an idea we were proud of. We also are excited about kickstarting the r/Welping community on Reddit, which we want to grow and have it join the ranks of popular funny story subreddits like r/CustomerFromHell. We think that filming our demo video at the diner and spending time thinking about the best way to convey our goals and motivations really created a great presentation as well.

What we learned

We discovered early that solid prompt engineering is the foundation of a successful Bolt-driven workflow. Spending time up front designing clear, modular templates and prompts let us iterate on new features faster than we ever expected. Also, working on multi-API integrations taught us to build reusable modules with robust error boundaries and rate-limiting. Reducing technical debt now will save hours of debugging down the road. We also learned the hard way that real-time functionality (Supabase) demands careful permission and privacy considerations, so we should implement strict RLS rules to keep user data secure and earn user trust. On the product side, we learned just how powerful a wild idea can be when we focus on rapid validation. Sketching the reverse-Yelp concept and the “Welping” companion helped us spot holes early and double down on what felt genuinely fun. We got a crash course in prioritizing scope, leaning into the features that made us smile, dropping anything that didn’t land, and celebrating small wins (like our first successful automatic Reddit post in r/Welping!). By the end, we knew how to turn a silly + random hackathon concept into a fun, shareable experience. Even if our subreddit has only a few members so far, we’re ready to grow it.

What's next for Welp

In the coming weeks, we’ll double down on polishing and iterating on our features. We plan to build the following features: Sentiment analysis so our system can highlight the most outrageous (or heartwarming) reviews; More features like image attachments and threaded comments; User and business profiles with proper access + controls; An API for integrations with businesses; And in the future, a Welp mobile app for iOS and Android.

On the community side, we’ll start seeding r/Welping with curated stories, host “roast nights” to draw first members in, and launch a lightweight onboarding app to guide new users toward posting. We’re lining up a partnership with Mel’s diner (where we filmed our demo video!) to pilot Welp internally, get feedback from real service workers, and develop a friendly moderation framework so r/Welping can scale into a go-to hub for funny + cathartic customer-service stories. Our video has already reached some people and we’re excited to see what’s next.