Sramana Mitra: You’re still selling completely direct? You’re not working with any of the major system integrators?
Matthew Elenjickal: We do. We’re partners with almost every system integrator that you can think of. We work with almost every system integrator out there including Accenture and Genpact .
We work with a lot of big software companies like JDA, Oracle, and SAP. Our solution is integrated into their systems. These are all the different channels that we are working on.
Sramana Mitra: What about team? You said you have about 200 people. Are these people in Chicago? Outside of the sales teams, is the rest of your team all in Chicago?
Matthew Elenjickal: We have right now, as see you speak, around 350 to 360 people. Half of that is in Chicago and the remaining half in India. Our development team is in Chennai. Within the team in the States, the sales team is spread out all over the country. But everything else – marketing, finance, operations, customer success, and support – is in Chicago.
Sramana Mitra: What’s happening in in the first quarter of 2019? Is there anything else that we should talk about in this story?
Matthew Elenjickal: Yes, we raised more money. We raised another $50 million. So far, we have raised more than $100 million right now.
Sramana Mitra: Why do you need so much money? You have significant sized enterprise contracts?
Matthew Elenjickal: It’s an opportunity because it’s a land grab situation. There’s no solution out there that’s offering what we’re offering. This is a wide space. We have some 30 plus reps.
When you have an opportunity out there, the tendency is to grab the market. Obviously, our decision was to put money into it and make it happen.
Sramana Mitra: Excellent execution and excellent identification of opportunity. I’m very happy to cover this story. Is there anything you want to add before we end the call?
Matthew Elenjickal: During the briefing meeting here with my team members, they mentioned about AI and machine learning.
Sramana Mitra: Are you doing stuff relating to machine learning?
Matthew Elenjickal: Yes. As part of the platform, we’re tracking half a million truckloads in a given time; that’s a lot of data that we’re collecting. In fact, we’re getting a GPS ping every 15 minutes. You can do the math, 15 minute pings for half a million shipments in any given day. That’s a lot of data.
Like I mentioned in the beginning, the market is highly fragmented. There are trucking companies of all sizes and technology and technical capabilities in the market starting with the big trucking companies, small trucking companies, to owner-operators where the owner is driving the truck.
There are blind spots in the supply chain whatever way you look at it. There might be areas where the cell phone reception is bad. There may be times when a driver doesn’t want to be tracked. There are blind spots all over supply chain. So the question is, how can we use this massive amount of data to fill those blind spots? That’s something we are doing.
Let me give you a good example. Let’s take a Walmart distribution center in Chicago. Because we have so many shippers shipping to that location, we can predict what the average wait time is in that location by time of day or week. Imagine having that data and incorporating that data for planning purposes.
If a carrier or if a trucking company knows that they’d wait for four hours by showing up at 3PM at this location but only two hours if I show up at 3:30PM, they will plan accordingly because they want to get their drivers back on the road as soon as possible. These are the kinds of decisions that they help companies make using machine learning and data science.
Another example. A lot of disruptions can happen. So we look 24 hours, sometimes 48 hours, or seven days in advance and we can tell you what type of impact it’s going to have on your shipment and the type of equipment you should use.
Let’s say you’re shipping water and water can be transported in both dry and refrigerator modes, depending on the weather and the temperature along the route. You can use less expensive equipment if the weather isn’t bad. That brings your transportation costs down. Those are just some examples of how we’re applying machine learning to bring more insights to our customer base.
Sramana Mitra: Good. It’s a very smart company. I can tell. I know enough about the logistics industry from my childhood and growing up in a shipping family.
I know it’s a very complex and very cumbersome industry. So I’m sure there’s a lot of opportunity for applying machine learning and big data related solutions to optimize. So very well done.
Mattew Elenjickal: Thank you.
Sramana Mitra: Thank you for your time.