Introducing Faretrotter 2.0

It has truly been a long time in the making.

I remember the exact moment in 2012 when I first thought of what would become Faretrotter — I was in Boulder, Colorado and I had just returned from a consulting trip to Belgium. Upon my return, we were in the living room in our small split level home off of Table Mesa in south Boulder. Nothing too out of the ordinary, but over a few glasses of wine, I shared with my friends the goings on of my latest trip.

The two week trip spanned three countries, two currencies, three languages, and seven modes of transportation – bus, flight, train, car rental, public transportation, taxi, and mitfahrgelegenheit. At the time, no tools were mainstream enough to make this planning easy and information readily available. So, to compensate, the preparation consisted of spreadsheets and multitudes of Google searches and translations.

“How’d you know how to do that?” asked one of my friends. I recalled the time I had spent in the area before, and knew it was something you could only known simply by having lived in the area before. As I answered, I remember feeling the gears turning.

There was an originating city, multiple arcs (modes) to get me to several other nodes (cities), and several more connecting from each of those. Compounded a few times over and I’ve arrived at my final destination. On top of that, all of these modes of transportation have several departures per day, over a few classes, and with various connecting cities. If you were to maximize things like convenience or comfort or minimize things like price or duration of travel time, combinatorics would tell you a trip like this could result in millions of combinations. College still wasn’t that far in the past at the time and as I was telling this story (while feeling the subsequent engineering gears turning), I can still vividly remember feeling the requirements of this being a quintessential Dijkstra’s algorithm, NP-hard, dynamic linear optimization problem. It composed all parts of the problems that really attracted me. The obsession with solving this problem has yet to leave.

Fast forward 7 years, involving a move to Atlanta, forming the company, researching, building MVPs, researching, forming partnerships, launching, studying, researching, relaunching, rebuilding, studying, researching, losing partnerships, rebuilding, relaunching, researching, rebuilding and relaunching. And we’re here. We’ve arrived.

The struggles that have ensued over the previous 7 years have seemingly accumulated into something far greater than the original idea that was conceived that night in Boulder.

As it would turn out, our suboptimal path has taken us through multiple iterations of what we wanted to test. We’ve managed to get our product in front of over 50,000 people in this time – executing hundreds of thousands of searches and holding thousands of conversations of how people expect to travel. And that has been the major battle all of these years – how to streamline a sophisticated algorithm into something that is useful to how travelers want to plan their trips?

And while we didn’t take the optimal path to where we are at today, it has been such an important trip.

Happily, I can introduce Faretrotter 2.0. Here are four things:

  1. We still search every mode of transportation – flights, trains, buses, ferries, ride shares, airport shuttles, public transportation, subways, and even dog sleds – although without live pricing like before. This is forthcoming.
  2. For travelers, there is more of an emphasis on human contextualization of these modes of transport. A ferry costs $15? Great, how can I get insider tips to make this information even more helpful?
  3. We now have an API that you sign up for today. This is for businesses who are looking to supplement their marketing initiatives, companies, startups, or developers who want to build apps using our data, or anybody else who might want to fill in the gaps.
  4. Every picture on our landing page? We’ve taken all of them. Just a personal touch 🙂