The Story

The narrative arc that ties everything together.

The Setup

Healthcare back-office is massive and broken. Utilization management alone is a multi-billion dollar function. And it's a mess — not because the technology doesn't exist, but because the incentives are misaligned, the relationships are adversarial, and the people making decisions are drowning in complexity.

Most people who try to fix this fail. Silicon Valley thinks tech solves everything. "We'll automate prior auth!" But if technology were the answer, Epic wouldn't be a piece of crap. The problem isn't automation — it's understanding what to automate and why and for whom.

The Insight

We see what others miss.

Most people look at utilization management and see "denial letters" or "prior auth automation." That's the surface. The real problem is the relationship between payer and provider. Incentives. Information asymmetry. Judgment under pressure.

If you solve symptoms, you build band-aids. If you don't understand the real issues, you fail.

We understand the real issues — because of who we are.

The Solution

Daisy AI: AI-powered services for healthcare operations.

Not pure software. Not just automation. AI + expert services. We're not replacing humans — we're augmenting them. Trust and judgment matter in healthcare. Our model accounts for that.

We start with utilization management. Land with health plans. Prove value. Expand.

The Journey

Now: Premera contracting (close Spring). AppriseMD late stage. Real traction, not just conversations.
Next: Expand to adjacent functions. Prove the model scales.
Later: Define the category. AI + expert services becomes the standard for healthcare operations.

The Moment

Why now — the AI threshold: LLMs can handle judgment-intensive tasks that were previously human-only. But you need healthcare depth to apply them correctly. Most don't have that.

Why now — the infrastructure moment: The tools caught up to the ambition. Claude Code, Vercel, Neon, GitHub — a two-person team can ship production software that would have required 10 engineers five years ago. This isn't scrappy. It's leveraged.

Coding in 2025 is different from coding in 2015. You don't need algorithms and data structures. You need to know how to work with AI, pick the right platforms, and ship. The talent gap closes because the nature of the talent needed has changed.

Why us: We bridge the gap that kills healthcare startups. Engineering + healthcare PE + native to the new paradigm. We see the real problems. And we can build — fast.

The window: First movers who get this right will define the category. That's us.