#StartupsEverywhere: Austin, Texas
#StartupsEverywhere: Jenny Yu, Founder & CEO, EcoPulse
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.
The AI Startup Helping Manufacturers Tackle PFAS and Supply Chain Risks
Austin-based startup EcoPulse uses AI to help large manufacturers manage complex supply chain risks related to Perfluoroalkyl and Polyfluoroalkyl Substances (PFAS), or “forever chemicals,” and compliance. We sat down with founder and CEO Jenny Yu to learn about her fundraising journey, the role of AI in environmental safety, and the regulatory challenges manufacturers face.
Tell us about your background. What led you to EcoPulse?
I’ve worked in Environmental Health and Safety (EHS) enterprise software and tech space for 14 years before starting EcoPulse. I was the General Manager of my previous company’s China operation in 2015, when I decided to leave Shanghai, China, and move to Baltimore, MD, in the U.S., to pursue my masters in business administration at Johns Hopkins. After graduation, the same EHS software company that I worked for hired me back to their HQ office in the U.S. to lead product innovation and management for manufacturing clients, and I spent years helping corporations manage environmental issues and compliance across global supply chains, especially with the latest AI and other new technologies.
Around two years ago, I decided to launch EcoPulse with my Co-Founder and Vice President of Marketing, both former colleagues. Backed by a team of great talents and an advisory board of eight senior industry advisors, we set out to close a massive gap in how companies handle PFAS and other similar supply chain risks using AI-driven software.
What is the core challenge EcoPulse is solving for manufacturers?
PFAS presents urgent, complex risks for manufacturers, including litigation and regulatory penalties. However, most industrial PFAS data remains untapped due to supply chain silos and labor-intensive analysis. EcoPulse helps manufacturers proactively identify and mitigate these risks through advanced data analysis.
For large companies with tens of thousands of product components, manually checking every material for PFAS is nearly impossible. Most rely on manual document review, supplier response, and sample-based lab testing. EcoPulse’s patent-pending AI solution leverages multiple specialized AI models, a proprietary knowledge base, and a risk assessment algorithm to rapidly analyze across materials and supply chain data to identify PFAS presence, together with detailed chemical and concentration information, regulatory applicability, and relevant research findings. This process helps EcoPulse clients follow due diligence procedures, save time, reduce mistakes, and make informed material choices.
Now, let's discuss your technology: how does it change the traditional approach to PFAS lab testing?
Traditional PFAS lab testing has been a very important method to collect data for compliance, but can be costly and impractical when evaluating hundreds of thousands of materials. Our technology doesn't replace these tests, but uses AI to sort and filter large amounts of data first. We apply four specialized AI models and our unique knowledge base to predict which materials are most likely to have PFAS, along with specifics and reasoning. The risk map provides prioritization and focus for companies to test only high-risk materials, making the lab process more cost-effective and yielding clearer results than conventional broad audits. Most importantly, with the detailed reasoning and evidence behind each AI assessment in EcoPulse, companies can now have a very organized, efficient, and defensible risk funneling process for PFAS risk review and management.
As more policymakers are thinking about AI regulatory frameworks, how are you navigating the changing policy landscape?
We continually refine our technology and maintain secure, compliant systems. Right now, there isn’t a national AI standard across sectors yet. Instead, each of our customers has their own rules, so we have to clear each customer's individual checklist rather than meet a single standardized bar. In the meantime, as a cloud solution, we needed to achieve SOC 2 Type II compliance, which we recently did. As our technology develops and customers refine their individual approach, we have to adapt to new things like 'AI risk assessments,' which are increasingly required by corporate buyers to determine which models you’re using, what guardrails you have in place, etc. I believe states or federal agencies are starting with high-risk sectors for AI regulations, i.e., Healthcare AI applications, and will then implement and expand.
How are you approaching intellectual property as an AI startup?
We do not build AI models entirely from the ground up, but we have a provisional patent protecting our proprietary knowledge base with the business and risk analysis algorithm specific to PFAS risks. This knowledge base organizes industry knowledge and experience into structured rules, enabling our AI to provide useful chemical risk insights to businesses. While our current provisional patent does not include AI coding, our hope is that as we dive deeper into improving different parts of the model, we will be able to package some of the fine-tuning parameters into a new scope for a patent.
What has your experience been with capital access and navigating the grant process?
It’s a tough market, but we have raised ~$200K in pre-seed funding from Austin investors and an accelerator in 2025. Most VCs want to see significant revenue growth before investing, so we’re also exploring non-dilutive options such as Small Business Innovation Research (SBIR) grants.
Pursuing grants brings its own set of obstacles. Navigating the grant process can be incredibly challenging without a deep understanding of the research-focused lens that these agencies require. The recent re-authorization of SBIR grants is a great opportunity. We’ve been researching and evaluating applicable and eligible grants for EcoPulse. We’ve been fortunate to work with domain experts who advise us in evaluation and application. It can definitely be effort-consuming as we are also trying to maintain a 12-to-15-month-long enterprise sales cycle and other priorities to grow the company.
Are there any local, state, or federal startup issues that you think should receive more attention from policymakers?
When it comes to policy, industry standards for AI could help improve trust among manufacturers, state agencies, and cities. Beyond that, there are major friction points between government reporting requirements and industry capabilities. States like Minnesota require PFAS reporting, but their digital platforms are often difficult for manufacturers to navigate. One of our goals is to support manufacturers in the reporting preparation and work, and to show policymakers that AI can lower the "regulatory burden" on industry.
As a woman-and minority-owned business, I’ve noticed that many companies' diversity goals have recently decreased drastically. Continued policy support for minority-owned startups would be a major boost for expanding innovation. We also need more "lower entry points" for government contracting programs that allow startups to pilot tools with agencies without the multi-year wait of an SBIR cycle.
What are your goals for EcoPulse moving forward?
PFAS is our first focus. Next, we aim to address wider supply chain risks and help companies find safer material alternatives. Regarding our company's projection and roadmap, we’re planning our seed round for early next year and aiming for a Series A in 2028 to fuel growth and expand internationally, particularly into the European Union, where environmental awareness and regulation are prominent.
All of the information in this profile was accurate at the date and time of publication.
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