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DAISY AI
AI-powered services for healthcare operations
Daisy AI combines deep healthcare expertise with AI to transform how health plans handle complex administrative work — starting with utilization management.
The Problem
Healthcare back-office is massive and broken. Utilization management alone is a multi-billion dollar function across payers. And it's a mess — not because technology doesn't exist, but because incentives are misaligned, relationships are adversarial, and the people making decisions are drowning in complexity.
Most startups that try to fix this fail. They think technology solves everything. But if technology were the answer, Epic wouldn't be a piece of crap. The real problems are incentives, relationships, and judgment under pressure.
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 — incentive misalignment, information asymmetry, judgment under pressure.
If you solve symptoms, you build band-aids. We understand the real issues — because of who we are.
The Solution
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.
First Product: Inpatient Status Assessment
When a patient is admitted, someone decides: inpatient or observation? This decision happens at T+4-8 hours — but no one applies criteria at that moment. Physicians decide without guidance. Case managers review 14-24 hours later, after the decision is locked.
What we built: Decision support that brings criteria-based reasoning to the decision point. Pulls clinical data automatically, applies Two-Midnight reasoning, produces a structured assessment showing what supports the status, what's missing, and what to document.
Why it matters: $2,000-$5,000+ revenue difference per case. Reduces denials, appeals, rework. Gives hospitals confidence to classify appropriately. Protects patients from observation status when inpatient criteria are met.
Why Now
AI capability crossed a threshold. LLMs can now handle judgment-intensive tasks that were previously human-only. But technology alone isn't enough — you need people who understand healthcare deeply enough to know where AI helps vs. where it creates liability.
The infrastructure caught up too. Claude Code, Vercel, Neon — a small team can ship production software that would have required 10 engineers five years ago. We're not under-resourced. We're leveraged.
Most tech companies don't have the healthcare depth. We do. The window is now.
Traction
| Customer | Stage |
|---|---|
| Premera (major health plan) | Contracting — close Spring |
| AppriseMD | Late stage — close Spring |
| HURC | Mid stage |
| McBee | Early stage |
Real customers engaging. Healthcare-slow, but moving.
The Team
Rare combination: healthcare private equity + engineering capability.
We bridge the gap that kills most healthcare startups. Tech people don't understand healthcare economics. Healthcare people don't understand technology. We have both.
Both founders build production software daily. That matters.
The Ask
[Amount] at [terms]
Use of funds:
- Expand team
- Accelerate customer acquisition
- Extend runway through first major closes
Milestones this round:
- Close Premera and AppriseMD
- Prove model scales
- Build pipeline for next stage
Contact: [info]
Design Notes
For when this becomes a designed PDF:
- Clean, minimal design
- One visual max (could be traction timeline or team photos)
- Contact info prominent
- Print-friendly (black/white should work)