Cedar Innovation, Technology & Agility

What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
Cedar’s AI story is one that’s familiar to all of us — the patients who face the nearly universal experience of receiving healthcare and managing what comes next: the bill. To deeply understand these needs, my team built a large language model-powered pipeline that transcribes and parses thousands of patient call recordings to gather the insights and data that fuel our AI voice agent, Kora. Kora provides patients with comprehensive and compassionate medical billing support, delivering personalized guidance on everything from balance inquiries to payment options — even when patients can’t fully articulate their problem.

Our blog title would be: “The AI That Listens When You Don’t Know What to Say.” Healthcare billing is an area where users are often confused, anxious and unsure of the exact problem they are facing. It’s why building AI for healthcare is different from typical consumer applications. Our patients aren’t looking for a specific product code or a simple return process — they need guidance, diagnosis and a path to resolution.

What was a monumental moment for your team when it comes to your work with AI?
One monumental moment occurred last year, when we released our AI chat assistant and were overwhelmed by positive feedback from the users of the product — call center agents who provided billing support to patients. We were excited to have the chance to speak with the call center agents during our over-the-shoulder sessions, where we observed how these individuals were using our new AI tools to supercharge their workflows.

We knew that we had something special when an agent exclaimed, “That’s exactly what we need. I couldn’t have said it better myself!” upon seeing an AI-generated suggestion to a patient inquiry. The qualitative feedback from call center agents and patients, along with the numbers that illustrated the efficiency gains brought by our solution, gave us the conviction we needed to double down on Cedar’s AI investment and make AI central to how we solve patient problems.

AI is a constantly evolving field. Very few people coming into these roles have years of experience to pull from. Explain what continuous learning looks like on your team. How do you learn from one another and collaborate?

Experience is helpful, but we also value persistence, creativity and problem-solving. Our strong team of applied AI scientists not only have deep expertise in AI, but many of them also have exposure to different aspects of healthcare billing, such as insurance claims data. That knowledge, paired with a drive to solve complex problems for patients and clients, is invaluable. We also welcomed a PhD fellow earlier this year, recognizing the importance of fresh perspectives. We’re also now hiring for a staff applied AI scientist.

What sets our team apart is our commitment to learning and collaboration. In our recent prompt tuning retro, we reflected on our experimentation process, surfaced insights and pain points, and outlined action items around tooling, workflows and architecture exploration.

Our weekly brainstorms are another cornerstone — providing space to share ideas and feedback at every stage, from experiment design to results review. This spirit of collaboration is what drives us to solve healthcare’s toughest AI challenges.