Simply - for businesses

We help reduce financial costs, identify impactful trends in your business, and generate detailed analytics for your business to leverage.

⚠️ What's the problem?

Krispy Kreme throwing out full tray doughnuts every night, which prompts us to think - what restaurants have to do this on a daily basis?

Interviewing some individuals and small business owners: restaurants/cafe would often over-order materials, attempting to predict trends, or just outright purchasing ingredients just in case customers would want them randomly. This bet on predicting trends with little analytics enables outright more food waste and makes business owners of all sizes hurt their pockets.

Data also shows that nearly 40% of all food that comes from businesses are often thrown out, perished, or just outright wasted due to over-ordering.

We estimate a business of a mid-size (think Chili's) will waste about $1,300 per month due to over-ordering and over-scheduling of employees. So the big question is, what if you had the power to predict how much you need for the next day, week, or month?

🙌 Inspiration

We understand that not all business owners are familiar with the idea of data analytics and how well to utilize them - that's why we named our product Simply, as we integrate easily within POS, and we provide datasets that are easy to understand from a layman perspective.

🔨 What does Simply do?

Simply utilizes impactful data from the businesses receipts regarding trends, popularity, and supply management to make predictive data for the businesses' next grocery haul, how many employees to station the next day, and understanding the popularity of items; which all help the business owner make more educated decisions financially, and save more food from going in the trash.

🥪 Our tech sandwich

We used NextJS (TypeScript & React), Python (back-end), AWS (db), with component libraries/helpers Radix (for icons), TailwindCSS, ESLint, Framer (for animations), Tremor (for graphs). Our front-end consisted of React, and our back-end hosted a self-trained neural network that helped predict given a CSV file. Our back-end additionally contained Tensorflow, Pandas, and SQLAlchemy. We handled API calls with Flask. Finally, we keep all of our data hosted in an AWS RDS.

Everything was contained in a fractal folder design pattern for NexTJS.

❗ Challenges we ran into while developing

Our main and primary blocker initially was connecting to AWS because we were all unfamiliar with database connections. Other challenges were developing with an optimal machine learning algorithm for our use case; which detailed all of our necessities and nitpicks, similar to Google's SEO engine.

💪 Accomplishments that we're proud of

We were able to put together a functional landing page, sign-up for small businesses and a working product, which we weren't able to have in previous hackathons.

We also obtained some data from a small business in Berkeley, which helped us with seeing if our proof of concept truly applied to businesses of all sizes.

🧑‍🎓 What we learned

We learned a lot about the food industry, its' large impact on climate change, and how volatile it is; we're all surprised no one made a viable solution before us. Development-wise, we learned how to utilize our own ML data with AWS RDS - we all previously worked with non-SQL databases, so this is all of our first times using SQL in conjunction with our program. Thanks Daniel!

🚀 What Simply could do given Skydeck investment

We want to integrate with POS systems worldwide, where we could easily gather data from receipt info, and make educated decisions for businesses of all sizes. There, businesses could opt-in to our program and receive detailed analytics with their only hassle counting their initial supply. We'll handle the rest after!

🔎 Sources

https://refed.org/food-waste/the-problem?gad_source=1&gclid=Cj0KCQjwj9-zBhDyARIsAERjds2bpnfIrdnKu3kvot7VcbQDwh0e0zHLfq5BMuR5LHOzSJTEAPYp704aAok0EALw_wcB

🫶 Honor & Mentions

Thank you to Jason for mentoring and teaching us how business structures are utilized, and being a main supporter of our engine which considers a numerous amount of factors. Thank you to Brandon and Matt for helping us pivot from our original idea and provide data as previous restaurant employees. Thank you to Sodoi Coffee Tasting for providing data!