Since its founding in 2009,
has been bringing big data to the place you’d expect to find it — bars.The app is a convenience for patrons, allowing them to settle their tab from anywhere, give candid feedback on service and even hail an Uber without leaving the app.
But for bars who integrate TabbedOut into their POS systems, it’s an absolute revelation. In addition to capturing customer criticism before it makes it to a scathing public review, TabbedOut gathers valuable data on who’s coming to the bar, how often and what they’re ordering. That helps them get to know their patrons, rewarding regulars and enticing others with discounts and personalized special offers.
TabbedOut is also sitting on a pile of precious market data for beverage companies, another source of revenue for the startup.
That means TabbedOut has accomplished the enviable feat of designing a slick app with multiple user groups and a diversified revenue model.
Lance Obermeyer recently returned to TabbedOut as CTO after a two year hiatus. He briefly joined the company in 2013 to reboot its engineering department.
We asked him about tech that makes TabbedOut tick, as well as his approach to challenges and what his team is working on next.
What technologies power your business?
Like most technology companies, there are variety of technologies in use. We have mobile apps for iPhone and Android, written in Objective-C and Java. We have back end components written in Python, Scala, and Java with MySQL and PostgreSQL databases.
What are the biggest tech projects your team is working on this year?
We’re working on a product that will generate even more data than we currently have, so we’re doubling down on data. We’re looking at revising our data warehouse and analytical stack and improving the efficacy of our classification and clustering algorithms.
From a market perspective, it will allow TabbedOut to enter a fast growing, adjacent market. This new product has both technical and business model innovations — we’re quite excited. It will use a similar tech stack to our existing products, although it will generate several orders of magnitude more data.
What are the biggest technology challenges you’ve faced in the past? How did you overcome them?
A development organization is a set of people, so human challenges are often more difficult than technical ones.
The new project we’re working on has a substantial data component, so there are analytical and machine learning topics we’ll need solve this year. Also, we’re a relatively old company at seven years, so we have to actively manage our technical debt.
What are lessons you’ve learned about working in Austin that other local entrepreneurs can learn from? Make sure your engineering organization has a plan for staying fresh with new ideas, practices, and technologies.
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