About half my career has been in healthcare in some form, and that’s really where Keebler’s story starts. My co-founder, Andrew, and I had spent time around healthcare operations and revenue cycle workflows, both professionally and personally as patients. The more we got exposed to how these systems work, the more obvious it became that there was a massive amount of important clinical information trapped in unstructured data.

We initially built a narrower point solution, essentially a revenue cycle tool focused on identifying documentation inaccuracies. But our value-based care (VBC) friends saw what we were building and recognized its potential for risk adjustment. That feedback led us to accelerate our move into VBC workflows in 2023, and the commercial response was immediate.

Since then, our internal mantra has stayed the same: don’t shy away from the hard problem. Healthcare has always mattered to me, and building something that helps fix a structural part of it is what drives our mission at Keebler.

Today, most chronic disease identification tools rely heavily on structured data, such as claims, billing records, and discrete EHR fields. But a huge amount of clinically relevant information never makes it into those systems cleanly. Historically, reviewing that information to find missed chronic disease codes required time-intensive clinical review, which simply didn’t scale.

That creates a real challenge for chronic disease management because organizations can only act on what they can see. As a result, important diagnoses can be missed, patient risk is under-documented, and reimbursement doesn’t always reflect the true complexity of the populations they care for.

Keebler Health uses large language models to process and synthesize that unstructured data at scale. We help surface evidence of chronic disease and potential missed diagnoses directly from the clinical narrative so clinicians and care teams can review and validate them before decisions are made. The goal isn’t to replace clinicians; instead, it’s to give them a more complete picture of the patient faster than would ever be possible through manual chart review alone.

For our customers, that leads to more accurate chronic disease detection, stronger audit readiness, and better visibility into patient populations that need intervention earlier.

What’s been exciting about working with Tau is that they immediately understood both the technical side of what we’re building and the healthcare implications behind it. That combination is rare. Enterprise healthcare organizations do thoughtful, rigorous diligence on their vendors, and having partners who are already trusted in the ecosystem helps create the kind of early alignment that makes those conversations more productive.
What I’m most looking forward to is figuring out what becomes possible when you combine aligned conviction with the right network and experience. Early-stage companies move fast, but the right partners can accelerate entire categories of conversations you otherwise wouldn’t have access to yet.