Launch HN: Cenote (YC W25) – Back Office Automation for Medical Clinics

Hey HN, this is Kofi, Kristy and Ajani, co-founders of Cenote (https://www.joincenote.com/). We provide medical clinics with AI agents to speed up their referral intake.

Before a specialist physician can treat a patient, they must collect data about the patient, determine if the referral meets medical necessity, and see if insurance will cover the procedure. This involves analyzing referral documents, coordinating with primary care providers for missing information, and verifying insurance coverage—before they can even see the patient. It’s a manual back-and-forth process that is time-consuming, prone to errors, and slows down patient care.

Cenote mostly automates this workflow. (“Mostly”, because sometimes a human-in-the-loop is needed—more on that below). We use LLMs, OCR, and RPA to extract and validate referral data, check for medical necessity, and initiate insurance verification—all in minutes, not hours. This allows specialists to focus on care, reduce administrative burden, and ensure faster, more reliable insurance payments.

One of us (Kristy) dealt with this after an emergency medical event she had a couple years ago. The time it took her to find a clinic that could receive her medical record and insurance exacerbated her injury. It seemed crazy to have to wait that long for what turned out to be the dumbest of technical reasons. The three of us became friends at a book club, got talking about this, and decided to build software to deal with it.

Cenote automates the back office for medical clinics. When a referral lands in a specialist’s inbox, our software kicks in. We first parse the document through an OCR. After that, we use an LLM to detect the pieces of data that our customer has told us they’re looking for. If we detect the referral is missing data, we send a message back to the referring provider asking for more. Finally, we integrate with our customer’s EHR (Electronic Health Record) via RPA or API and place the document and extracted data in its appropriate location.

The OCR returns confidence intervals. If the LLM reasons over OCR that it is not confident about, we flag this in the UI to the end user and ask a human to review before moving forward.

We entered this task thinking we would have to work on a lot of fine-tuning / ML infra, but the tech needs turn out to be a lot more elementary than that. For example, we have spent a lot more time creating a history-page view of previously submitted files than we have spent training our own data. Many clinics still rely on faxed (!) referrals, and even well-funded practices use obsolete workflows.

While we provide a UI for clinics to upload documents and for human-in-the-loop intervention, our system can also function in a headless manner. By this, we mean that all core functionality—data extraction, EHR integration, and even back-and-forth communication with referring providers—does not explicitly require a UI for user interaction.

In terms of pricing, we charge an annual SaaS fee and a one-time implementation fee. We don’t have one-size-fits-all pricing on our website yet, but we’ll get there eventually.

If you have medical clinic experience, we’d love to hear your thoughts! And everyone’s feedback is welcome. Thanks for reading!

Comments URL: https://news.ycombinator.com/item?id=43280836

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https://news.ycombinator.com/item?id=43280836