Revolutionizing Home Design: An AI-Powered House Planning Platform
My final-year B.Tech capstone — a full-stack platform that turns a family's budget, plot, and lifestyle into personalized house designs, ML cost estimates, and a verified builder marketplace.
Namith K P
· 4 min read
Building a home is one of the biggest decisions most families ever make — and one of the most opaque. Professional architectural services are expensive and out of reach for a lot of people, especially in the affordable-housing segment. So for our final-year B.Tech capstone at APJ Abdul Kalam Technological University, my team set out to answer a simple question: what if you could describe your budget, your plot, and how your family lives — and get a real, optimized house design back?
That became Revolutionizing Home Design, a full-stack, AI-powered house planning and visualization platform.
The problem we wanted to solve
Traditional home planning has three pain points:
- Cost is unpredictable. Most people don't know what their house will actually cost until they're deep into construction.
- Good design is gated. Architects and quality planning aren't accessible to everyone, particularly for low- and middle-income families.
- Finding trustworthy people is hard. Architects, contractors, workers, and material suppliers are scattered, and quality is a gamble.
We wanted one platform that bridges all three — from the first idea to the people who actually build it.
What it does
You enter your plot dimensions, budget, room count, family needs, and design preferences. From there the platform handles the rest:
- AI-generated floor plans optimized to your spatial constraints
- ML-driven cost estimation so you know the realistic price up front
- A building-material recommendation engine matched to budget and use
- Energy-efficient & sustainable design suggestions
- Government housing-subsidy guidance to support affordable housing
- A verified marketplace of architects, contractors, workers, and suppliers
- Real-time messaging between homeowners and providers
- An AI chatbot for design assistance along the way
How it's built
The platform spans a web app, a mobile app, and an ML/AI backend:
- Backend & AI — Python, with machine-learning models for cost estimation and natural-language processing (Hugging Face) powering the design assistant.
- Mobile — Flutter / Dart for a cross-platform app.
- Web — a responsive dashboard (Bootstrap) for onboarding and management.
- Data — MySQL.
My role spanned architecture, the backend, and the AI modules — wiring the design-generation and cost-estimation pipelines into the product and shaping how the web, mobile, and ML pieces talk to each other.
Why the AI matters here
The interesting part wasn't bolting an LLM onto a form. It was turning fuzzy, human inputs — "a 3-bedroom home for a family of five on a tight budget, with room to grow" — into concrete, constrained outputs: a floor plan that respects the plot, a cost estimate grounded in real material prices, and material choices that balance budget against durability and sustainability. That's a chain of models and rules working together, not a single prompt.
Recognition
The work was accepted and presented at the International Conference on Cognitive Informatics, Engineering & Technology 2026, organized by Vidyaa Vikas College of Engineering & Technology (Autonomous) in collaboration with OSIET, Chennai and Samarkand State University, Uzbekistan (Tiruchengode, Tamil Nadu — 28–29 March 2026).
If you want the full detail, here's the project report and the presentation slides.
Team
Built with Gopika Gireesh N G, Rinsha Ashraf, and Sayanth K, submitted in partial fulfillment of the B.Tech in Computer Science & Engineering at Vimal Jyothi Engineering College, Kannur.
Looking back, this is the project that taught me the most about shipping something end-to-end — data model, ML pipeline, mobile, web, and the messy human problem in the middle. If you're working on applied-AI products and want to compare notes, reach out.