#StartupsEverywhere: Alex Reichenbach, Co-Founder, Structify
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.
A New Vision for Data Infrastructure
In the busy tech scene of Brooklyn, NY, a startup called Structify is changing how businesses handle their information. Founder Alex Reichenbach’s approach cuts through the traditional complexity of data management by building custom data pipelines that any user can interact with in natural language. We sat down and talked to Alex, who detailed how the company uses AI to automate the role of a data engineer, AI policy, the importance of compute credits to early-stage startups, and the need for STEM talent development.
What led you to Structify?
Machine Learning (ML) research has been the foundation of my career, with my first patent in the field awarded in 2017. My early career was in academia, where I worked in the Krishnaswamy labs, applying ML and AI research to single-cell RNA drug discovery. Later, I joined a computer vision lab at Matician, a robotics company.
Building on these experiences, I moved to a small investment bank, where I quickly recognized that many of their data challenges could be automated. Seeing firsthand how the data team struggled to obtain the necessary information, I realized I could leverage my AI and ML background to automate the data engineering work. This insight ultimately led me to create Structify.
What is the work you all are doing at Structify?
Our core mission is to make data accessible to smaller companies that have historically been unable to afford hiring for that role, even when it was desperately needed. We serve any organization with a variety of eclectic data sources that need to be formatted and structured in a specific way. Our customers come from a wide range of industries from construction to finance and even deep-sea mining.
Our product automates the role of a data engineer, focusing on the extract, transform, and load pipeline. Our key innovation is using code generation. You simply tell us the transformation you want in natural language, and our system writes the code for you. It then tests the code until it has a highly standardized pipeline. Users then receive a clear, high-level visualization of the pipeline, along with the detailed code, which is in the background, but can be examined if the user wishes to look under the hood. The ultimate goal is to empower individuals with little to no technical knowledge to acquire the skills of a data engineer.
What role is AI playing in democratizing access to data engineering?
AI is core to everything we do. We utilize Claude, a large language model for code generation. We're not doing anything super wild there; we're just riding the wave of what's state-of-the-art. For more specific tasks, such as web scraping and navigation, we utilize our own models. Those are built by fine-tuning existing large models using proprietary datasets that we invested heavily in creating.
How would legislation regulating foundation models or fine-tuning impact you?
There are a few things that come to mind. First, policy discussions that frame larger tech companies as adversaries are shortsighted, because they have been incredible partners to us. We have significantly benefited from the support of larger cloud providers, such as Amazon Web Services (AWS) and Google. Together, they provided us with $700,000 in compute credits, which was crucial for fine-tuning our models and helping our company grow before we secured significant venture capital.
Second, we support additional funding for STEM talent development programs. The state of New York has established a startup funding program that matches state money with private investments, yet these initiatives need extended time to produce results. The process of education and training requires extended periods, so programs need ongoing funding to achieve lasting development.
What are your goals for Structify going forward?
Our primary goal for Structify is to empower every business with the power of a large, sophisticated data team, similar to the one at the largest investment bank or broadest logistics company. We want companies to be able to process their data and access the information they need without being limited by their resources. Our ultimate purpose is to use what we call "commoditized intelligence" to help businesses of all sizes make better decisions.
We love empowering small businesses to build things quickly with our product, things they previously couldn't have done.
All of the information in this profile was accurate at the date and time of publication.
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