Company Overview
Clint Lotz is Founder at TrackStar AI, a company that optimizes lending with AI-powered insights. Learn more at trackstar.ai.
Can you tell us a little about your background before starting at your company?
Before founding TrackStar AI, my career spanned banking, mortgage technology, healthcare IT, and automotive finance. I started in community banking, where I helped modernize mortgage underwriting by replacing manual processes with digital workflows, dramatically reducing loan processing times. Throughout my career, I was consistently drawn to solving operational problems through technology and data. That experience ultimately shaped how I approach innovation today: identify inefficiencies, understand the underlying data, and build practical solutions that deliver measurable outcomes.
How did you start at your company? What were the first steps you took to get it off the ground and how did you identify the need for your product/service in the market?
TrackStar AI was born from nearly two decades of experience in the credit ecosystem. After acquiring and growing one of the leading SaaS platforms for credit, our team accumulated a unique dataset of more than 30 million real-world credit dispute outcomes. As we analyzed that data, we recognized a much larger opportunity. Rather than helping consumers after credit errors had already caused harm, we could use machine learning to identify likely inaccuracies before they became missed lending opportunities or customer friction.
The first step was validating that hypothesis with our historical data and building predictive models capable of identifying credit reporting errors in real time. That evolved into Revelar, our flagship AI solution, which enables financial institutions to proactively identify credit reporting anomalies using data they already have, without requiring changes to their underwriting models or existing decision processes.
What innovations or unique features set your company apart from others in the industry?
What makes us unique is the proprietary data and experience behind our technology. Revelar is trained on more than 30 million historical dispute outcomes, allowing it to identify patterns that traditional credit scoring models were never designed to detect.
Rather than replacing underwriting, Revelar complements it by uncovering hidden opportunities within an institution’s existing applicant and customer base. Financial institutions can improve approval opportunities, increase customer engagement, and enhance financial wellness while maintaining their existing risk models. We believe the future of AI in financial services isn’t about replacing human decisions, it’s about providing better intelligence to make those decisions more informed and equitable.
What has been the most effective strategy for scaling your business?
Our growth strategy has focused on solving a specific, measurable problem for enterprise financial institutions rather than trying to be everything to everyone. We work closely with banks, credit unions, and fintechs to validate outcomes through pilot programs before expanding into larger deployments.
Participating in industry accelerators, building strategic partnerships, and demonstrating measurable business outcomes have been instrumental in opening doors. Rather than leading with technology alone, we lead with business value: helping organizations increase engagement, improve lending opportunities, reduce customer acquisition costs, and strengthen customer relationships using data they already possess.
Looking ahead, what are your goals for the future of your company?
Our vision is to become the standard intelligence layer for identifying credit reporting inaccuracies across the financial services ecosystem. In the near term, we’re expanding partnerships with banks, credit unions, fintech platforms and credit bureaus while continuing to enhance our AI capabilities and broaden the ways financial institutions can leverage predictive credit intelligence.
More broadly, I believe AI will play an increasingly important role in making financial services more accessible and more accurate. Our goal is to help ensure that qualified consumers aren’t overlooked because of preventable data issues, while giving financial institutions better tools to serve their customers with greater confidence.
