At Fooda, we believe people should love and look forward to lunch every day. Our tightly knit Product, Data Science, and Engineering teams turn this belief into a reality by dreaming up and developing meaningful technology for lunch eaters and the local restaurants that serve them. Adjectives used to describe our team: friendly, intelligent, highly technical, and fun. Regardless of seniority, everyone takes personal ownership.
We're a proving ground for Chicago's future tech leaders. Our senior team members place heavy focus on mentorship and we all genuinely care about seeing each other succeed
Sanjit, VP of Engineering
Dig deeper into our technology platform and our projects to see how we handle all the moving pieces that go into selling millions of meals across the country at a rate that has been growing exponentially since we started it all in 2011.
Fooda POS let's us capture key data related to our customers and events. It fuels the data science team's application of machine learning and allows us to make data driven decisions as a business. Without it, we'd be flying blind.
Every intelligently run business wants individual purchase data. But linking real world purchsaes to individuals is a considerable challenge that requires Fooda's own POS.
Overcoming the challenge of designing and rolling out a POS was top of mind. Building technology that can process more than a hundred million dollars per year requires world class reliability.
Last, but not least: most of Fooda's volume happens all at once. Turns out most office workers tend to buy lunch at the same time. Successfully handling peak demand requires thoughtful infrastructure and performance decisions.
We developed or own internal, highly scalable enterprise grade POS system. Optimized for performance and availability, it's built on a distributed architecture and supports offline transactions. Because we don't believe a bad internet connection should stop customers from buying lunch.
The POS backend tracks every order, event log and user click in real time to provide the best possible user experience.
To keep the line moving, we have a strict requirement on the amount of time to process an order. We use a distributed infrastructure to maintain low latency and high availability. We are built on top of a messaging architecture that links various systems over RabbitMQ. Because you really don't want to hold people up when they're waiting for al pastor. Trust us. You don't.
Reduce Time to Delivery
When Fooda shifted into major growth mode, technology quickly became the linchpin of our growth. We wanted technology to push the business forward, not hold it back. A tight, agile TDD framework proved to be a game changing solution that sped us up, kept out bugs, and ultimately impacted the whole company.
Historically, the team had an over-reliance on manual QA. As the engineering team grew, the QA team needed to grow in equal proportions. Testing and deployments got slower and it hampered us from iterating quickly.
It was taking one full day of manual regression testing (2 QAs) to sign off outgoing releases. Deploying code to production took up to four hours, with painful manual quality control nad deployment workflow steps.
Because of this overhead, the team could only release 1-2 times per week.
The Fooda engineering team loves to solve problems and we're enthusiastic about finding new and improved ways to work together. With that attitude, the team was able to make serious progress with improvements.
The development team strongly believed that test driven development (TDD) was the way to go. During weekly lunch & learns, engineers gave presentations on how to write good tests and how to incorporate TDD into day to day coding.
We migrated the devops stack to Kubernetes running on AWS; created internal tooling to automate testing and deployment steps, and trained engineers how to navigate their own devops issues.
All regression testing has been automated and all new features ship with automated tests. Various levels of unit and integration tests are automatically running on proposed code changes. Today we only need two QAs for twenty engineers and we're deploying multiple times per day in just 15 minutes.
In addition to TDD, we established monitoring and alerting as the cornerstones of the development process. The development team creates health checks, alerting, and dashboards to ensure the health and up time of their own feature sets. Our team finds out about production issues way before a user ever sees them.
Fooda AI: Scheduling
Fooda AI solves a massive puzzle. Thousands of Popup restaurants visit different workplaces every day. Fooda's secret sauce: matching them all in a way that keeps customers happy, maximizes restaurants' sales and minimizes food waste.
Before Fooda AI, putting the puzzle together every month was tedious, manual and kept getting harder as the company grew. We needed a smart, automated way to create the best possible restaurant lineup for each workplace - balancing customer preferences, cuisine variety and restaurant availability. We also wanted to provide a sales forecast to the restaurants to help them bring the right mix of menu items each day.
First came the data. We needed access to a comprehensive data store of restaurant sales history, consumer engagement metrics and external factors like weather forecast and restaurant availability. We chose Redshift as a data warehouse along with Airflow, an open-source ETL tool, for a reliable, structured data pipeline.
Then came the AI. We developed continuous learning algorithms run in Spark clusters to create optimal restaurant schedules that consider 150+ features and accommodate constraints.
We're now providing real-time food sales forecast to our restaurant partners to help them maximize sales and minimize waste. Users are loving their lunch more than ever, our restaurant partnerships are strong and for Fooda's operations teams, migraines have gone away.
Active Feature Development
Fooda for Restaurants Web App
Fooda and our restaurant partners share a lot of information (e.g. scheduling, event details, sales and payments data). This used to be mostly handled through email. To strengthen communication - and for the sake of everyone's sanity - we are building a self-service, mobile responsive web app that provides easy access for restaurants to view their information on any device.
Corporate Subsidy Program
As a perk, many companies want to pay for part of their employees' lunches at Fooda Popup. One challenge - tracking employee purchases if the Popup is in the lobby and accessible to all tenants in the building. The team is creating a first-of-its-kind solution: a web app where corporate clients can administer their subsidy programs, a feature in Fooda's consumer app where employees can redeem subsidies, and a feature in the restaurant POS for validating subsidies when they are presented at checkout.
Popups Around Me
We want to make it easy for users to find other Fooda Popups outside of their normal workplace location. During a hack week, the team built a "Popups Around Me" prototype, which impressed the rest of the company. The prototype has since been turned into a consumer app feature. Users can now see which Popup restaurants are nearby and what they are serving for lunch. To make them easy to find, walking directions and photos of the location are provided.
Fooda Meal Planner
Everyone hates it when a Popup restaurants sells out of a popular item during service. Restaurants feel the pain of a lost opportunity and it's a major let down to the customer. Fooda's Data Science and Analytics team built the Fooda Meal Planner, which guides restaurant partners on the ideal number of lunches they should prepare each day. The goal is for restaurants to sell their last remaining menu item at the very last minute of service. No more hanger.
Fooda's Technology Leaders
Meet Jason, Our CPO
Jason leads product, engineering, data science and marketing at Fooda. He is part of the C suite leadership team that has grown the company 50x in sales over the past 5+ years. Prior to Fooda, Jason founded three startups, funded by Lightbank and Redbox. As an MBA graduate of Northwestern's Kellogg School of Management, Jason started his career in private equity and management consulting at Bain. His undergrad was in Engineering at the University of Michigan. Earlier in life, Jason was a game away from playing in the little league world series baseball tourney on TV.
Meet Sanjit, Our VP of Engineering
Sanjit moved to the U.S. in 2004 with $500. Today, he has 12 years of software engineering experience and a masters degree in Computer Science from the University of Illinois (he earned a Ray Ozzie scholarship). Sanjit spent five years at Zillow, where he grew the mobile and API teams from three developers to 20, and helped build 5-star rated apps. The apps were so awesome that the team won two Webby Awards. Zillow acquired StreetEasy, and Sanjit went there to help develop the engineering culture. Before Zillow, he fell in love with open source technology building IE at Microsoft, which was very closed source back then (and yes, he met Bill Gates).
Meet Patrick, Our VP of Data Science
With 14 years of experience in the field, Patrick joined Fooda in 2017. He came to us from a machine learning startup that he founded in Seattle called Lumidatum. Fooda was an early Lumidatum client and Patrick just couldn't shake us. With his team in tow, he hopped on a plane, moved to Chicago, and launched our Data Science and Analytics team. Previously, Patrick ran a worldwide team of BI Engineers and Data Scientists for Amazon with offices in 5 countries. Before Amazon, Patrick launched the analytics team for WMS Gaming (acquired by Scientific Games). He started his career in consulting for a few years at Accenture and Dunn Solutions Group. Patrick has his MBA from Northwestern's Kellogg School of Management. He studied computer science as an undergrad.
Fooda is available in Atlanta, Austin, Boston, Chicago, Cincinnati, Columbus, Dallas, Denver, Detroit, Hartford, Houston, Los Angeles, Orange County, Milwaukee, Minneapolis, Nashville, New York City, Philadelphia, Phoenix, San Antonio, San Jose, Seattle, Stamford, and Washington D.C.