The Emerging Technology group at RevUnit works hard every day to ensure that RevUnit is bringing innovative solutions to our clients’ products that help those clients #WorkBetter. 

We sat down to chat with a couple of those team members, Co-Founder and Chief Technology Officer Michael Paladino and Associate Data Scientist Jacob Bovee, about how they bring their varying experiences to the team and how their work in machine learning and artificial intelligence is propelling RevUnit and our clients into the future.

First, some background:

 

Jacob Bovee

When did you first get involved in the tech world?

Jacob: I started coding at a pretty young age, just making video games and stuff. I would say at about nine, I was developing games primarily, then came that organic shift into the web where you want people to see what you’re doing.

J: Developing things has been a passion of mine for a long time. Even in middle school, I had some projects that steered me in a certain direction. I developed an internal social network for my middle school. I started seeing people respond to my work. I took a less human direction in high school as I was a bit more interested in glancing behind the curtain, going real deep into technologies like machine learning.

When did you make the shift into the industry sphere and how did you end up at RevUnit?

J: After high school, I started working professionally at a Founders Fund backed startup where we developed a large data science platform and were looking at the concept of dimensionality reduction using neural networks. From there I co-developed a product to classify protein crystals using Computer Vision that was later used by Genentech.

J: Later, I was working with a company in Vegas called Teamvvork, which was later acquired by RevUnit. When RevUnit bought Teamvvork, it was a natural transition to move into the Emerging Technology group which has been initially focused on machine learning and artificial intelligence.

 

Emerging Tech at RevUnit

Why is RevUnit focusing on emerging tech, and what have you done to realize that focus?

Michael: At RevUnit, we build digital products that help our clients #WorkBetter. This means our products have to continue to get smarter, more personalized, and more predictive. We see a number of emerging technologies such as machine learning and artificial intelligence, IoT, virtual reality, augmented reality, and blockchain acting as building blocks to enable those better products and experiences.

M: Initially, we focused on going deep with machine learning and artificial intelligence (“ML/AI”). That meant building out the team, mapping out the tools and platforms available that could help us add value really quickly, and figuring out exactly what types of ML/AI challenges we wanted to take on. Today, we’re deep into a number of those implementations and continuing to work to communicate the value of the technologies to both our existing and new clients.

What are some of the most interesting projects you’re working on?

J: We developed a demo for a talk that Michael gave at BoxWorks 2017 that used Box as a pipeline for a machine learning application. For the demo, when a driver’s license was scanned in and uploaded to a particular folder, Box would call our app via a webhook that would then shoot it off and use machine learning technologies to extract information from the license. I thought that was interesting, conceptually, because many companies are sitting on these types of data repositories, and we can create some huge wins for them by using ML/AI on top of that existing data.

M: We also worked on a feedback mechanism for an employee communication app. At RevUnit, our mantra is “Build small, learn fast, iterate often.” That means we listen to our users, take the feedback, and iterate on that and make the product better. Therefore, most of our products have some kind of user feedback mechanism. If someone has something negative to say, we want to know immediately so we can address the issue. So for this application, we used IBM Watson to do a sentiment analysis on the feedback we get so we can classify the feedback as positive or negative and kick off a workflow for how we respond.

M: Jacob is also actively working with Amazon Lex and Microsoft’s Bot Builder SDK to build out a conversational interface to simplify customer interactions for one of our clients. The primary conversion for this client consists of a web form with a large number of fields. In some cases, conversational interfaces have led to 300% increases in conversions. So, obviously that’s an opportunity for us to make a big impact on both the end user and on the company’s operations.

M: Beyond those, we’re working with a number of our clients on everything from Alexa Skills to apps that aid in food production planning to apps that predict wait times at registers. There’s a ton of opportunity in the space, and we’re excited to be ideating and executing solutions.

woman talking to smart phone

 

How do you work together? What have you learned from each other?

M: Jacob and others on the team are technical experts in this space. I’m not a trained data scientist or machine learning engineer, so I am spending time with them learning the guardrails of these technologies and where to best apply them.

J: Michael has this great grasp of technology, but he also has a deep understanding of what’s pragmatic and what’s going to work. I’ve also learned a lot about asking “why?” instead of just asking “how?”.

J: Working and cooperating together has been an integral part of our relationship. Generally speaking in previous roles, I haven’t consistently had to explain what’s going on because nobody cares. But Michael cares a lot. I think it’s a great dynamic in which I get to answer questions which make me think and reevaluate, and at the same time, Michael is giving me support in how it’s going to be relevant in a business aspect.

M: I’m naturally inquisitive. I want to understand. I love the technology, and I’m still a developer at heart. In order for me to communicate the value to the rest of our organization and our clients, I have to understand the technology. So I love getting to dive into the details with Jacob and our other team members.

What challenges have you come across in the field of emerging technology?

J: Machine learning is still in its infancy. As an industry, we are still laying the groundwork and infrastructure and proving it out. At the same time, this is happening parallel to an unbelievable amount of hype and to machine learning really only now coming together as a field that looks like it will be as important as the internet.

M: Artificial intelligence and machine learning are at the peak of inflated expectations. Seeing through the hype for the substance, and also helping our clients understand what is hype versus reality is a big challenge right now.

M: Additionally, getting access to accurate, sanitized data can still be a challenge. Data scientists can still spend up to 80% of their time in data preparation.

man with ipad on head

 

What is the future of the emerging tech department at RevUnit?

M: Our group identifies the relevant technologies through a scorecard approach. It’s a continual process of identifying the new technology, learning how it’s going to fit inside of RevUnit and our clients’ enterprises, building it into our core capabilities, then moving on to the next technology. Given the pace of innovation in technology, this will likely be an infinite process 🙂

J: I genuinely think that emerging technologies are all reaching maturity a lot sooner than people are anticipating. They are going to be core to any business and certainly any business in the tech space. The beauty of the emerging tech department is that we get to ensure that RevUnit is ahead of the curve.

 

Responses have been edited and condensed for clarity.