Thinai
AgTech startup using AI/ML to better predict crop quality.
Thinai's mission is to empower agriculture companies through advancements in technology and research. Based on the CEO's own research studies at UC Davis, Thinai was founded based on the potential that advancements in artifical intelligence can be used to help better predict crop quality and yield through environmental factors, therefore reducing unnecessary losses and waste rampant in the agriculture industry.
Project Summary
Founder wanted assistance in developing an initial MVP to present to investors and prospective clients. I joined the team in 2023 where I spearheaded the development process, including architecture, authentication, and deployment. Together, the company was accepted into SVG Ventures Thrive Academy's 3rd cohort and received a $150,000 pre-seed funding offer from NEC X, a prominent Silicon Valley VC.
The Technicals
Powered by NextJS, Vercel, and AWS
The initial technology was build as a few models housed on the founder's laptop built in R. It was decided we would run multiple AWS Lambda functions using a custom R runtime to allow the founder to continue updating the models until a dedicated data scientist could convert it into Python. Thinai's dashboard was built using NextJS and deployed on Vercel.
Based on customer discovery calls and user feedback, we were able to lock down the most valuable features, such as expandable date ranges and saved predictions to be used with contract negotiations between the barley maltsters and farmers.
Gallery
Ready to impress with your next project?
I'm here to help: linkedin github rwhui.tech@gmail.com