Inspiration
As a first-time homebuyer, I was overwhelmed by the complexity of home inspections—missing critical issues felt like a huge risk. I saw fellow buyers struggle to understand what to check, document defects clearly, or communicate with inspectors. That’s when I decided to build a tool that demystifies the process, putting control back in the hands of buyers.
What it does
HomeInspection Checklist is a self-guided tool for home buyers to conduct thorough inspections. It organizes 200+ checklist items across 10 key home systems, lets users mark statuses, attach photos/notes, and uses AI to score risk severity. It also streamlines inspector coordination, task tracking, and report generation for seamless sharing with agents/sellers.
How we built it
We started by mapping core user workflows—from starting an inspection to exporting a report. We built the frontend with React/React Native for responsiveness, used Node.js/Express for backend logic, and integrated TensorFlow for AI risk classification. PostgreSQL stored inspection data, while AWS S3 handled photo storage, and PDFKit powered report exports.
Challenges we ran into
The biggest hurdle was refining the AI risk-scoring logic to accurately classify defects (critical, major, minor, cosmetic) based on real-world home inspection standards. We also struggled with optimizing photo upload/linking for mobile use, as users often inspect homes on phones in low-light conditions—requiring large touch targets and reliable upload retry logic.
Accomplishments that we're proud of
We created an intuitive interface that’s accessible to non-experts, with jargon-free guidance for every checklist item. The AI risk summary accurately flags deal-breaking issues (like foundation cracks or faulty wiring), and the one-tap PDF export has been tested to generate comprehensive, shareable reports in seconds. The app is fully responsive, working seamlessly on phones and tablets.
What we learned
We deepened our understanding of home inspection industry standards and user-centered design for niche tools. We also learned to prioritize mobile optimization for field-use apps and how to balance AI automation with user control—ensuring the tool is helpful but not prescriptive.
What's next for this project
We plan to add offline mode (critical for homes with poor connectivity), integrate with local home inspector directories, and expand the checklist to include region-specific risks (e.g., flood zone assessments, wildfire safety checks). We also want to add a feature to compare inspection findings against local repair cost data to aid negotiations.
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