# Project export: BuildPulse

This document was generated by HackStack to give an AI agent context about a hackathon project. Sections are labeled with their provenance; content marked as truncated was cut to keep this document small.

## Project metadata

- Hackathon: CruzHacks 2026
- Tagline: See the delay before it sees you.
- Devpost: https://devpost.com/software/buildpulse-6va4hk
- GitHub: not linked
- Video: https://www.youtube.com/embed/0Jw6Bx-l-ng?enablejsapi=1&hl=en_US&rel=0&start=&version=3&wmode=transparent
- Result: winner ([MLH] Best Use of MongoDB)
- Team: contributor stats unavailable

## Devpost submission (written by the team)

### Inspiration

Construction delays cost the industry $1.6 trillion globally every year. We discovered that 98% of projects face delays, yet 52% of contractors still track progress with paper clipboards and highlighters. Enterprise solutions cost $50,000+, pricing out small contractors who need help the most. We built BuildPulse to democratize AI-powered construction tracking—making it accessible to everyone from solo contractors to major developers.

### What it does

BuildPulse is an AI-powered construction progress tracking platform that: Analyzes construction photos using computer vision to automatically detect completion percentage Supports 3D building models (GLB/GLTF) with interactive zone assignment for mesh-level tracking Predicts project delays using machine learning on progress velocity data Calculates financial impact showing exact dollar costs of delays ($15K+/day) Generates AI video reports for stakeholders with zone-by-zone breakdowns Identifies what's complete vs missing (e.g., "Framing done, roofing still missing")

### How we built it

Frontend Next.js 16 with App Router and React 19 for a modern, performant UI React Three Fiber + Three.js for interactive 3D model visualization with orbit controls Tailwind CSS 4 for styling Radix UI components for accessible, polished interactions Recharts for progress tracking visualizations Backend MongoDB + Mongoose for scalable project and zone data storage Auth0 for secure authentication MongoDB GridFS saved the project by helping us store 3D models AI/ML Google Gemini 2.0 Flash for construction photo analysis — detects building components (roof, walls, foundation, etc.), estimates completion percentage, and lists what's done vs. missing Gemini AI also powers our smart zone naming suggestions when users click on 3D model meshes Linear regression for progress velocity predictions and estimated completion dates OpenNote SDK for generating AI-narrated video reports for stakeholders

### Challenges we ran into

3D mesh selection: Getting click-to-select working on GLB models required building a custom SelectionManager with raycasting and material-based grouping AI accuracy: Tuning Gemini prompts to reliably identify construction stages (framing vs. finishing) took extensive iteration Real-time sync: Keeping 3D viewer state, database, and UI in sync when analyzing photos required careful state management File size limits: 3D models exceeded Supabase's limits, so we implemented base64 encoding with MongoDB storage for the hackathon

### Accomplishments we're proud of

✅ Full working prototype with 3D model support, AI analysis, and video report generation ✅ Smart component detection: Gemini correctly identifies building elements like "Roof", "Foundation", "Exterior Wall" from mesh geometry ✅ Financial impact calculator that shows exact delay costs based on project budget ✅ Scroll animations and premium UI with glassmorphism, 3D progress cylinders, and interactive cards ✅ End-to-end flow: From project creation → 3D upload → zone assignment → photo check-in → AI analysis → video report

### What we learned

Gemini's vision capabilities are incredibly powerful for domain-specific analysis when given proper context Three.js in React requires careful memory management and camera positioning for user-friendly 3D experiences Construction industry pain points are massive—there's genuine need for accessible AI tools Hackathon scope management: Starting with core features (AI analysis) before polish (animations) was crucial

### What's next

📱 Mobile app for on-site photo capture with offline support 📊 Multi-project dashboards for construction firms managing portfolios 🚁 Drone footage analysis for large-scale site monitoring 🤝 Contractor marketplace connecting delayed projects with available workers 🌱 Carbon footprint tracking to quantify sustainability impact of early delay detection Built With next.js react three.js react-three-fiber tailwind-css mongodb mongoose auth0 google-gemini opennote vercel recharts radix-ui

## README (from the GitHub repository)

No README available.

## Detected evidence (automated analysis)

No repository was indexed for this project. Claimed technologies below could not be checked against code.
- Google Gemini (technology) — claimed on Devpost, not found in the code
- MongoDB (technology) — claimed on Devpost, not found in the code
- Next.js (technology) — claimed on Devpost, not found in the code
- React (technology) — claimed on Devpost, not found in the code
- Tailwind CSS (technology) — claimed on Devpost, not found in the code

## Codebase structure

No repository index available.

## Key source files

No repository index available; no source files included.