CleverBuildAI

Inspiration

A lot of people in the US have the need to attach an accessory dwelling unit (ADU) in their backyard. The Accessory Dwelling Units (ADUs) market in the United States is estimated to be around $127.1 billion. The current process to find and plan for installation of an ADU is very time-consuming.

What it does

User inputs address, Pick options, e.g.: budget, layout (1b1b/studio), etc. (Use budget to inform sizing.) Retrieve data from Google Maps Satellite imaging Dwelling location Lot size Interpret building codes to identify buildable regions on lot Min. distance from the main dwelling, fire safety, utilities, etc.

How we built it

Used Lovable, Google-Maps, OpenAI, LlamaIndex

Challenges we ran into

We needed to call a machine vision model to help us extract structured data. Moreover, because we need to calculate the accurate ADU constructible area based on the ADU policies of the user's residence, we have inserted a mathematical calculation module.

Accomplishments that we're proud of

We are proud that we have created an application that makes it much easier and faster to find and select compliant ADUs and will thereby increase ADU adoption and create less expensive housing for the many people in California and other locations across the United States who have a hard time affording housing.

What we learned

• Llama Parser + agent dramatically increased the information retrieval performance
• Front-end and back-end development and connection ## What's next for CleverBuildAI

Built With

• agent
• chromadb
• flask
• google-maps
• gpt3.5
• gpt4v
• javascript
• llamaindex
• llamaparser
• llm
• lovable
• openai
• python