In the latest installment of this series profiling RET portfolio companies and their executives, we talk to Tripty Arya, CEO and Founder of Travtus.

What is the inefficiency in the real estate industry that makes Travtus necessary? 

There is something of a disconnect in real estate because it is largely a transactional asset class, but it also has the burden of operations. If it was a truly efficient asset class, it would be more tradable and scalable. 

Real estate’s inefficiencies come largely from opacity of information and the significant labor cost associated with operations. Travtus aims to create an operational solution that enables property managers to scale across a larger portfolio without a significant increase in their workforce.

How do you solve that problem with Travtus?

We took a variety of approaches when exploring the problem of how to scale operations across a growing portfolio. First, we took a human approach, then a software and database approach, and eventually landed on the machine learning approach. 

The technology we ultimately developed enables property teams to be much more efficient by taking many of the “long tail” property management issues off their plate. The machine-learning-powered system first reviews email correspondence, service requests, and other information to gain a 360-degree understanding of the property and how residents interact with it and then works as a virtual customer service representative that responds directly to resident communications.

When we were in the discovery process, we realized that machine learning has the highest chance of success because it thrives when there’s a large variety of fairly small tasks that have to be done. 

We first use our model to identify these tasks not just for an industry but on a company, community, and even resident level. This information allows transparency into the operations, which then assists with the next steps in the journey. Most long-tail tasks are best handled conversationally; the items that are more repetitive then become candidates for automation. Through this process, there are operational and performance indicators that become the byproduct of the process and additional inputs for efficiency. 

Essentially, we solve the problem of scalability by bringing machine learning to insight and decisions, instead of a pure focus on workflows. 

How does your technology differ from your competitors’?

The shift in machine learning as a mainstream branch of technology has really allowed us to build a category-defining solution with Travtus. In other words, we have the privilege of being able to take a new approach to business problems without the bias of old technical stacks. This is very important to our growth plans as a business and also a very clear differentiator for our product. 

Our unique approach is to solve our clients’ problems using a machine learning platform and architecture as opposed to relational databases — resulting in a much closer replication of the human approach. Unlike any other solution, we focus on handling everything rather than deploying departmental solutions – ultimately improving a broad variety of day-to-day management tasks. This is crucial for our clients, for whom separation of tasks is not always easy and cost transformation is dependent on a variety of task automation as opposed to purely departmental solutions.

If you weren’t a proptech executive, what would you be doing?

Before I was a proptech executive, I was a practicing architect and real estate developer, so I would probably still be doing that. If you ever need someone to learn how to solve problems creatively, put them on a construction site with a deadline and a budget linked to their own money!

What is the hardest thing about building a winning team?

The hardest thing about building a winning team is learning how to say “no”. There is a risk with every business that the pressure to perform or grow can open the door to people who may not be the best fit for your team at that stage. Think of your business as an athlete and your team as its coach. The needs of the business change as it performs better, meaning the type of team it needs also changes. One of the hardest parts of running a business is ensuring that the team you have is constantly evolving professionally and personally with the business. I’m extremely proud of the team we’ve created at Travtus, which was built with patience and awareness of our strengths and weaknesses as both individuals and as a team. 

What advice would you give to a young entrepreneur just starting out in rent tech or proptech?

Take the time to understand the business of real estate before diving head-first into the technology. Many young founders develop solutions based on their experience in a specific team within the industry or from a consumer perspective. This can be dangerous because oftentimes they are missing the whole picture. For example, if you are selling rent tech, your business needs to understand what it means to own and operate all those homes holistically. Once you can do that, the second most important thing is to ensure the solution you’re working on is scalable.

There’s an old trope about real estate being slow to adopt technology. Do you think that is still an accurate characterization?

Technology adoption is usually synonymous with change management. The pace at which any new tools or innovations are adopted is usually linked to the need for and value of that change. The real estate industry is currently dealing with many headwinds of change, and I think if the technology innovations available offer practical solutions to the current challenges owners and operators are facing, adoption won’t be a problem.

What’s next for Travtus?

It’s business as usual at Travtus. We are currently executing on our ten-year plan which includes continuous research with product and market expansion. In the coming year, we’re hoping to create tools and insights from our machine-learning platform that make decision-making and operations simpler for owners. We want to better leverage our data to help operators learn, train, and optimize their workflows, resulting in lower costs and better retention. This will involve taking complex decision-making processes and distilling them into simple assessments and insights using our predictive technology. 

We will also provide operators with an online platform to access useful knowledge, providing them with the skills and information they need to make decisions quickly and accurately, without sacrificing quality. Ultimately, our goal is to make decision-making and operations effortless for our operators.