#StartupsEverywhere: Constanza Gomez, Co-Founder, Sortile
This profile is part of #StartupsEverywhere, an ongoing series highlighting startup leaders in ecosystems across the country. This interview has been edited for length, content, and clarity.
From Textile Waste Researcher to Tech Startup Pioneer
Constanza Gomez initially saw her interest in textile waste as just a hobby. However, that changed when she co-founded Sortile, a technology platform designed to identify textile types in order to facilitate recycling. We spoke with her about her passion for textile waste recycling, the difficulty securing public grant money, the role of AI, and the company’s future.
Tell us about your background. What led you to Sortile?
I'm originally from Chile. I'm an industrial engineer who studied computer science, but my first job out of college was doing research into retail companies in Latin America. This is how I got introduced to the problem of textile waste, created not only throughout the value chain but also when things reach the end of their lives. I became interested in why the default solution was depositing textile waste in landfills or turning it into waste energy. This was 10 years ago in a country that's not known for its sustainability practices, so most people thought I was insane. I am a researcher at heart, and researching textile waste and trying to understand what was happening became my hobby.
Later, I got married in America, moved to the States, and went to Columbia for my MBA. During the first few weeks, one of my professors encouraged us to talk about a problem we were passionate about, so I talked about textile waste. At the end of class, somebody came up to me and asked if I would want to work on something in this space together. That person is now my co-founder. Her name is Agustina and she's also Chilean by chance. We ended up spending a year while I was doing my MBA and she was doing her MPA using Columbia’s network to talk with hundreds of companies in the textile space including recyclers and collectors. During this time, I noticed that one of the main problems with textile waste is sortation. Recycling has always been material-specific. You recycle glass and cardboard separately, and it's the same when it comes to clothing. The issue is that nobody knows what our clothing is made out of, which makes sorting by material extremely difficult and expensive.
We started by trying to collect data around textile sortation, to understand if it was worth it and what types of textiles were out there. In that process, we realized there was no primary data in the space. Nobody was doing much sorting because it was so expensive. So I called a friend with a PhD in material sciences and he suggested using a spectrometer and trying to build algorithms to determine the materials. We did that for some time and somebody eventually suggested using our tech for more than just collecting data. We got some grants, developed the hardware a lot further, and here we are today with the clients interested in separating.
What is the work you all are doing at Sortile?
We have two sides to our product. The first is the actual hardware, a little box that a material is placed on, and in less than a second it determines what materials the item is made out of. The second side is from a data perspective. We feed all of that data to the management of these sortation centers. They may not be sorting for a specific material today, but we still give them the entire information breakdown on the material flows in the facility and provide them with data about what they could potentially sell to a recycler. We also have relationships with recyclers who have vetted our technology and will purchase from our clients directly. So, we are helping them through the data to kind of offload a larger part of the textiles that they're currently getting. Most of our clients are US-based, but we recently started to expand to Latin America.
How do you approach manufacturing the device?
We manufacture the hardware in-house. It doesn’t make sense to contract manufacturing at the volumes we sell today and over time we have reduced the time it takes to build. Some components are locally sourced and some are imported. We import those components because they are specially made and we just can't get them here.
What has been your experience with obtaining grants and raising capital?
We haven't been able to get public government grants. We've tried, but the process is hard. Right now, we've gotten close to half a million in grants from private entities. We tried for the Small Business Innovation Research (SBIR) program twice, and will probably try again at some point. Just about everyone I talked to hires consultants—who take about 10% of the grant—and right now, I can't commit to that.
I feel like it's gotten better between the first time that we applied, versus now. I have more understanding of all the steps and different agencies you need to be registered with. Another complicated aspect is the difference between agencies. We've looked at the Environmental Protection Agency SBIR, and it's completely different from the National Science Foundation SBIR. The information needed is also sometimes hard to come by. Don't get me wrong, it's not like it's impossible to find. You just have to navigate a little bit and it takes time that we’re short on.
The feedback that we got from our SBIR application was that it was written as if we were pitching to an investor or a venture capitalist. This makes sense because that is about 95 percent of how we raise money. This made it difficult to find ways to improve as it was not the content or innovation that was the issue but rather the framing of it.
How does your product use AI and what do you want policymakers to know about it?
We use AI—machine learning—for the identification side. We have machine learning models that take the data from sensors in the hardware and can predict the materials, the color, and more. We don't have a lot of machine learning when it comes to data analysis. Instead, we provide all of the data and a nice dashboard so users can look at different sorts of data—productivity metrics, sales, and market tracking.
With the rise of Gen AI, everyone seems to be terrified of AI which stems from a lack of education around AI in general. This means most of the policy discussed is done from a place of fear. There's a ton of stuff with AI that's positive, and some of it doesn't even have to do with Gen AI. One of the things that concerns me is when policy and AI are discussed that policymakers only focus on how to regulate big corporations and they unintentionally hurt a ton of businesses and startups that use AI for things that have nothing to do with what those companies are doing.
What are your goals for Sortile moving forward?
Our main mission is to divert most textiles from landfills, so we're trying to work really with more governments. We mostly work with the private sector because of how the textile collection system works in the US.
Chile is really important to us, not only because it is my home country but because it's the largest importer of secondhand clothes from the U.S. Chile and Guatemala import most of the textiles that come out of the U.S., which is 17 percent of the world's textiles. Our big goal between now and the end of the year is how can we empower countries that are having to deal with a problem that they did not cause and turn that narrative around to create a positive, economic case.
All of the information in this profile was accurate at the date and time of publication.
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