#StartupsEverywhere: Kyle DeSana, Co-Founder, Siftree
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.
Turning online discourse into actionable insights
Many businesses lack comprehensive cultural insights to inform their strategic decisions. Kyle Desana and his co-founder utilize advanced algorithms to analyze cultural signals on social media, allowing businesses to make informed decisions and predict key metrics. We sat down with Kyle to discuss his company, how AI is accelerating innovation, and the need to enhance capital access in startup ecosystems like Chicago.
Tell us about your background. What led you to Siftree?
I have a background in data science and analytics. Over time, I noticed that many tools in the space were primarily focused on surface-level brand tracking, such as monitoring names, keywords, and sentiment. These tools have gaps, particularly in terms of cultural insights and connecting those insights to business outcomes, such as revenue or forecasting. I saw an opportunity to approach things differently, and that drive to do things better ultimately led me to Siftree.
What work are you all doing at Siftree?
Narratives and opinions shape consumer behavior because we're all influenced by those around us, whether nearby or by what we see on social media. What happens in the global landscape shapes our patterns and beliefs, impacting what we purchase, watch, and listen to.
At Siftree, we don't track keywords or listen to specific hashtags. We analyze what’s happening in the world, connecting that external data to internal data to help businesses understand what signals are creating impact from a demand perspective. Companies can use this to inform supply chain decisions, how they market their products, and the narratives they want to construct on social media.
We create a custom algorithm for our clients, which—much like the algorithm on your social media feed, shows you things you’re interested in—shows the most relevant intelligence from a company perspective. Currently, our customers range from direct-to-consumer brands to agencies, media, entertainment, gaming, and some political organizations.
Can you describe how the system is built?
Some companies may have robust data science teams but most lack digestible inputs for forecasting and analysis. We deploy advanced machine learning algorithms to cluster content from around the Internet and identify patterns, streamlining insight generation. Our approach utilizes unsupervised learning for clustering and classification, along with Natural Language Processing tasks such as sentiment analysis and named entity recognition, which extracts notable entities—like brand names, influencers, or celebrities—from the data.
Then, we utilize large language models (LLMs) to sift through the data and obtain survey-level results from that observational data. For example, we can determine the percentage of the population that is for, or against something, and get that answer through a natural language question. You could ask that question, and you wouldn't necessarily need to conduct surveys or run polls; we create that level of sophistication just through a natural language query.
How does AI enable you to iterate and innovate faster? How are recent advances changing the startup ecosystem?
The incorporation of AI tools significantly enhances our efficiency; we can now iterate in terms of days through design tools that allow us to prototype quickly. LLMs are like commodities, open-source models are available on platforms like Hugging Face, and we can just plug, play, and test out which works best for us. It has been able to speed things up tremendously, taking months or weeks of development cycles down to just a few days.
I’ve noticed it is also changing how early-stage investors are evaluating companies. It's not necessarily about whether they are an AI company or if the product is AI-enabled. Instead, they’re checking if they’re using all these AI tools because companies that are will be much more profitable. They're able to do everything with higher margins, and a lot more speed. Investors want to know not only your tech stack, but how you're going to market, how you're doing the design, how you're analyzing your finances, and more. They're looking to see if you are using the newest AI tools to condense everything you're doing from a cost and time perspective.
What is your experience raising capital within the Chicago ecosystem, and what can policymakers do to support it?
Chicago is a city with many older, wealthy residents, but they tend to be very risk-averse when it comes to investment. As a result the LP (Limited Partner) ecosystem in Chicago is limited, and VC funds are a lot smaller than on the coasts. Here in the Midwest, an $80 million fund is common, and going to be relatively selective. They're not able to make earlier bets due to the size of the fund, and they can't have a high risk tolerance, because they don't have many bets to place. Whereas on the West Coast, they usually raise $2 billion in funds and distribute checks widely. A founder there can get a check based on a quick win. I like to call it the "quit your job check," where many individuals are tinkering with something and need about $100,000 to take the leap to quit their job, which happens a lot faster there. If we could speed up that flywheel in Chicago, it would help. There’s only so much you can do with tax incentives and credits, so policymakers might have to consider other structural changes to get that capital off the sidelines and into startups.
What are your goals for Siftree moving forward?
Our primary goal is to position ourselves as the leading platform for businesses seeking comprehensive cultural insights to inform strategic decisions. There's market research, there's consumer intelligence, and they all use social listening, but we’re using it to predict the real impact on revenue, demand, and supply chain. We want to be the intelligence bridge every company uses to understand anything from product innovation, to forecasting, and prediction.
All of the information in this profile was accurate at the date and time of publication.
Engine works to ensure that policymakers look for insight from the startup ecosystem when they are considering programs and legislation that affect entrepreneurs. Together, our voice is louder and more effective. Many of our lawmakers do not have first-hand experience with the country’s thriving startup ecosystem, so it’s our job to amplify that perspective. To nominate a person, company, or organization to be featured in our #StartupsEverywhere series, email advocacy@engine.is.
