Candormaps

Candormaps- Safety alerts
Candormaps- Review mode

Inspiration

The idea for Candormaps was born from a very personal friction—wanting to leave honest feedback about places I visited without risking awkwardness, retaliation, or privacy loss. I also saw how people around me struggled to report local safety issues discreetly or anonymously.

Candormaps emerged as a way to fix that. The platform was built to empower individuals to share anonymous reviews and safety alerts while ensuring authenticity through proximity verification—a concept so novel it led to a patent-pending mechanism for proximity-aware feedback validation.

I hadn't built a full-stack app in years, so this hackathon was a chance to explore what's possible using just prompts and momentum—what we now call "vibe coding." It was built across 3 weekends in off-hours, with zero mockups or specs—just curiosity, AI, and flow.

What it does

Candormaps is a privacy-first, location-verified platform for community feedback and public safety reporting.

Key Features:

🕶 Anonymous Feedback: Users rate businesses/places without revealing their identity. 📍 Proximity Verification (Patent Pending): Feedback & reports are accepted only if the user is physically near the location. 🚧 Safety Alerts: Users report local hazards (dark spots, poor lighting, etc.) anonymously. 🧠 AI-Powered Moderation & Sentiment Analysis: Screens content for abuse and extracts customer sentiment. ✍️ AI-Suggested Business Responses: Helps businesses reply to reviews with empathy and clarity. 📊 Business Dashboard: Verified owners access feedback, sentiment trends, and response tools. 📲 Real-Time SMS Notifications: Alerts businesses when new feedback is posted. 💳 Stripe Payments: Enables premium dashboard and analytics access.

How we built it

Frontend: React + Tailwind CSS Backend: Supabase (PostgreSQL, Auth, Storage, Edge Functions) Location: Google Maps API AI: OpenAI (Moderation, Sentiment, Response Generation) SMS: Twilio Payments: Stripe

All features were implemented with prompt-based building on Bolt.new, without traditional coding setups.

Challenges we ran into

Designing proximity checks without compromising privacy. Handling GPS variations on mobile, especially for edge location cases. Building moderation workflows that scale while keeping abuse out. Keeping the UI modern and intuitive without Figma or manual CSS. Managing secure user verification (for business claims) while maintaining anonymity.

Accomplishments that we're proud of

✅ Built a fully functional full-stack product using just prompts and no boilerplate.

✅ 21 pages, 12 components, ~6,045 lines of code, all generated in Bolt.new.

✅ Developed a patent-pending idea for proximity-aware feedback validation.

✅ Implemented a real-world-ready product that addresses public trust, business sentiment, and road safety—entirely from scratch.

What we learned

Prompt-based development is real and surprisingly efficient when paired with AI and modern platforms. Proximity-based workflows add integrity to user-generated content. Privacy-first designs resonate with both users and businesses. AI moderation and sentiment analysis can enhance trust and streamline feedback systems. Small projects can lead to real-world innovation when guided by problems that matter.

What's next for Candormaps

🎯 Better AI tuning: Refining moderation and sentiment accuracy. 🧪 Pilot testing: Deploy in a few communities and businesses to collect feedback. 💬 Multilingual support for regional adoption. 🔐 More security layers to enhance user data protection and business verification.

🤝 Open Contribution: I’m happy to donate the IP and project to any NGO or non-profit working on transparency, safety, or community tech.