Bridging Health Insurance Gaps with AI: An Interview with Joseph Schneier, CEO of Circle

In this exclusive interview, Joseph Schneier, CEO and founder of Circle (formerly CircleEngage.ai), shares insights on building AI-powered healthcare solutions that bridge gaps between insurers, providers, and patients. His company specializes in data integration and analytics that make healthcare more accessible to vulnerable populations.

Please click here to watch the full recorded interview.

The Inspiration Behind a Healthcare AI Company

What specifically inspired you to found Circle (originally Trusty.care) and focus on healthcare technology? What problem were you most passionate about solving?

"My journey to founding Circle began with a deeply personal mission. After working in patient behavioral change and seeing firsthand how fragmented our healthcare system is, I recognized that millions of vulnerable people—particularly older adults and those with limited incomes—were falling through the cracks of a complex system.

The original inspiration came from observing how difficult it was for people to navigate health insurance decisions. So many Americans don't understand their coverage options or how to maximize their benefits. I was particularly concerned about older adults who often face these decisions with limited support and high stakes.

When we started as Trusty.care in 2018, we focused on helping insurers and brokers better serve Medicare beneficiaries. But we quickly realized the problem was bigger than just sales—it was about creating meaningful connections across the entire healthcare journey. That's why we developed a comprehensive platform that uses advanced data analytics and AI to simplify complex healthcare processes."

Evolution of a Healthcare AI Platform

Circle evolved from Trusty.care to a comprehensive healthcare technology platform. Could you walk us through this transformation and how your vision expanded from sales enablement to encompassing the entire healthcare journey?

"The evolution from Trusty.care to Circle reflects our expanding mission. We initially set out to improve the sales process for health insurance, particularly Medicare plans. Our early platform helped agents better match seniors with appropriate coverage options.

But as we grew, we discovered deeper systemic issues. Insurance selection is just one moment in a person's healthcare journey. The real challenges occur when people try to use their benefits, connect with providers, understand their coverage, and manage their ongoing care.

This realization led us to develop a more integrated approach. We built out CircleCompare for sales operations, CircleActivate for engagement, CircleCommerce for commission processing, and our InnerCircle analytics suite—all powered by our Clara.ai machine intelligence backbone.

The rebrand to Circle in 2024 represents this broader vision of creating a fully connected community in healthcare, driven by data. Our name now reflects what we were always building toward—a seamless circle of care that brings together insurers, providers, brokers, and most importantly, patients themselves.

Today, we're not just helping people select the right plan; we're helping them navigate their entire healthcare journey by making data work better for everyone involved."

AI Integration Challenges

When developing the Circle platform, what were the most significant challenges in integrating AI capabilities into your healthcare solutions? How did you approach building an effective data and intelligence ecosystem that could deliver real value to users?

"Building AI for healthcare presents unique challenges unlike any other industry. First, there's the data problem—healthcare information is often siloed, inconsistent, and spread across different systems with varying standards. We couldn't just apply generic AI approaches; we had to build solutions that could work with messy, real-world healthcare data.

One major challenge was creating systems that could understand the relationships between different healthcare concepts. For example, connecting diagnosis codes with treatment options, provider networks, and insurance coverage requires deep domain expertise embedded into our algorithms.

Another significant hurdle was developing AI that respects both regulatory requirements and human decision-making. Healthcare isn't just about efficiency—it's about people's lives. So we designed our Clara.ai platform to augment human expertise rather than replace it.

Our approach focused on creating what we call 'pragmatic AI'—solutions that solve real problems for specific users rather than trying to build general-purpose technology. We spent countless hours with insurance professionals, healthcare providers, and consumers to understand their workflows and pain points.

The breakthrough came when we built systems that could incrementally learn from operational data while delivering immediate value. For example, our platform helps match consumers with appropriate healthcare plans while simultaneously learning patterns that improve future recommendations.

Perhaps most importantly, we recognized that AI is only valuable when it's accessible to users who aren't technical experts. We've invested heavily in creating intuitive interfaces that hide complexity while allowing users to benefit from sophisticated analytics."

Responsible AI Development

Healthcare involves sensitive data and diverse populations. How has Circle approached issues like data privacy, inclusivity, and transparency while building solutions for groups like older adults and those with low incomes?

"Building technology for healthcare requires an exceptional commitment to responsible practices, especially when serving vulnerable populations. Our approach begins with privacy by design—we architected our systems from the ground up to protect sensitive information while still delivering valuable insights.

For diverse populations like older adults and those with low incomes, accessibility and inclusivity aren't just nice-to-have features—they're essential requirements. We've conducted extensive usability testing with these groups to ensure our interfaces are intuitive and accommodate various accessibility needs.

Transparency is another cornerstone of our approach. We're careful to design our AI systems so they provide clear explanations of recommendations rather than functioning as black boxes. Users need to understand not just what is being recommended, but why—especially when it involves healthcare decisions.

We're also mindful of potential biases in healthcare data. Historical disparities in healthcare access and treatment mean that training data often reflects these inequities. We've implemented rigorous processes to identify and mitigate these biases in our algorithms.

Another important aspect is ensuring our solutions actually reach underserved communities. We've worked with community organizations and designed specialized pathways that address unique barriers faced by low-income individuals navigating complex benefit systems like Medicare and Medicaid.

Finally, we're committed to measuring impact beyond just business metrics. We track how our solutions affect healthcare access and outcomes for vulnerable populations, which helps us continuously improve our approach to responsible AI development."

Unexpected Challenges

What was the most unexpected technical or market challenge you encountered while scaling an AI-driven healthcare solution, and how did overcoming it reshape your approach to innovation?

"The most surprising challenge we faced wasn't technical but cultural. We underestimated how deeply ingrained traditional workflows were in healthcare organizations and how difficult it would be to introduce AI-driven change, even when the benefits seemed obvious.

We built what we thought was an elegant solution for automating eligibility verification, but discovered that many organizations had developed complex human processes around these tasks. Employees were concerned about their roles, and managers worried about losing institutional knowledge.

This experience taught us that successful healthcare AI isn't just about algorithms—it's about change management. We had to rethink our entire implementation approach, focusing more on gradual transition with hybrid workflows that allowed human expertise to remain valuable alongside AI assistance.

Another unexpected challenge came from regulatory complexity. Healthcare regulations vary significantly across states and are constantly evolving. Building systems that could adapt to these differences while maintaining compliance required much more flexibility than we initially designed for.

These challenges fundamentally reshaped our approach to innovation. We now practice what we call 'collaborative AI development'—bringing stakeholders into the process much earlier and designing systems that can be customized to fit existing workflows rather than demanding immediate wholesale changes.

Perhaps most importantly, we learned that technology adoption in healthcare isn't linear. Organizations may enthusiastically adopt certain AI capabilities while resisting others based on factors that aren't immediately obvious to outsiders. Understanding these dynamics has been crucial to our success."

Advice for Healthcare AI Entrepreneurs

Based on your experience founding multiple companies and now leading Circle, what three critical lessons would you share with entrepreneurs looking to develop AI solutions in the healthcare space?

"First, domain expertise matters more in healthcare than in almost any other industry. Many entrepreneurs enter healthcare with strong technical backgrounds but limited understanding of the industry's complexities. The most successful healthcare AI companies have deep clinical and operational knowledge embedded in their teams. If you don't have this expertise yourself, partner with or hire people who do—and listen to them, even when their insights conflict with your technological vision.

Second, build for the healthcare system we have, not the one we wish we had. Too many healthcare startups create solutions for an idealized healthcare environment where data flows freely, incentives are perfectly aligned, and adoption is driven purely by outcomes. The reality is messier—healthcare organizations have legacy systems, complex reimbursement structures, and risk-averse cultures. Your solution needs to work within these constraints while still delivering value.

Third, prioritize provable impact over technological sophistication. Healthcare stakeholders are increasingly skeptical of AI hype. What matters is demonstrating meaningful improvements in costs, outcomes, or experience that can be verified through rigorous measurement. Be prepared to prove your solution's value in ways that resonate with healthcare decision-makers—whether that's ROI for business leaders, clinical validation for providers, or improved experiences for patients.

If I could add a bonus fourth lesson: patience is essential. Healthcare innovation cycles are longer than in many other industries. Relationships matter, trust takes time to build, and sales cycles can stretch for 12-18 months. Build this reality into your financial planning and expectations. The entrepreneurs who succeed in healthcare are those who can maintain focus and adaptability through these extended timelines.

For those passionate about making a difference in healthcare, the rewards are worth the challenges. Few industries offer the opportunity to apply cutting-edge technology to problems that so directly impact people's lives and well-being."

Joseph Schneier is the CEO and founder of Circle, a healthcare technology company specializing in data integration and AI-driven analytics. Prior to Circle, he founded Cognotion and has served in advisory roles for numerous healthcare and technology organizations. He holds a certificate in Health Care Economics from Harvard Business School Online.