DAISY AI
AI-powered services for healthcare operations
Thomas Startz & Michael Yuan thomas@daisyai.com | michael@daisyai.com
One-liner
We combine 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 is a multi-billion dollar function across payers
- Incentives are misaligned between payers and providers
- Nurses and case managers are drowning in complexity
- Denials, appeals, rework — pure administrative waste
Why Most Startups Fail Here
They think technology solves everything.
- They automate symptoms, not root causes
- They don't understand the economics or the relationships
- They build software for workflows they've never done
The Insight
We see what others miss.
| Surface view | Real view |
|---|---|
| "Automate denial letters" | Payer-provider relationship dynamics |
| "Prior auth automation" | Incentive misalignment |
| "Tech problem" | People + process + trust problem |
The moat isn't what we know. It's how we see.
The Solution
AI + expert services for healthcare operations.
- Not pure SaaS — services wrapped in software
- Not replacing humans — augmenting them
- Not generic AI — deep healthcare expertise
What we do:
- McKinsey work: strategy, problem identification, process mapping
- Palantir work: build the software, run parts of the business
- Teach customers to build — create AI capability inside their orgs
First Product: Inpatient Status Assessment
Decision support for inpatient vs. observation determination
The gap:
- Status decision happens at T+4-8 hours
- No one applies criteria at that moment
- Physicians decide without guidance
- Case managers review 14-24 hours later — too late
What We Built
- Pulls clinical data automatically (vitals, labs, diagnosis)
- Applies Two-Midnight reasoning in real-time
- Shows what supports the status, what's missing, what to document
Impact: $2,000-$5,000+ per case. Fewer denials. Less rework.
Why Now
Three things converged:
1. AI crossed a threshold
- LLMs can handle judgment-intensive healthcare tasks
- But you need domain expertise to apply them correctly
2. Infrastructure caught up
- Small team can ship what took 10 engineers five years ago
3. Payers are under pressure
- Humana, United stock prices down
- Medicare/Medicaid cuts coming
Traction
Pull, not push.
| Customer | Type | Stage |
|---|---|---|
| Major West Coast health plan | Commercial | Contracting (Spring) |
| AppriseMD | Outsourcing | Late stage |
| HURC | Outsourcing | Mid stage |
Health plan context:
- UM automation is one of their three AI pillars
- Was on 2027 roadmap — we pulled it to 2026
Why Payers?
Yes, they're slow. Here's why we're there anyway:
- The pain is on their side — they employ the UM nurses, bear the cost
- They're under unprecedented pressure — stock prices declining, Medicare cuts
- They can't attract tech talent — we fill the gap
Our approach: pull, not push
- Find what's 1-2 years out on their roadmap
- Accelerate it for them
- Don't sell — partner
The Team
Rare combination: healthcare PE + engineering + both build
Founder 1
- Princeton engineering → Warburg Pincus healthcare PE
- Bridges business and technology in the same mind
Founder 2
- Healthcare investing background
- Built technical fluency through the partnership
- Ships production software daily
Why this matters: Most healthcare startups fail because tech people don't get healthcare, or healthcare people can't build. We have both — in both founders.
The Flywheel
The tools caught up to the ambition.
Coding in 2025 ≠ coding in 2015:
- Work with AI, not write from scratch
- Two founders shipping production software
- Not under-resourced — leveraged
What this means for customers:
- If we can develop this capability internally, we can do it for them
- We teach them to build, not just use
- That's stickier than any software
The Ask
Raising $2-3M
Use of funds:
- First FDE hires (expand delivery capacity)
- Accelerate customer acquisition
- Extend runway through major closes
Milestones this round:
- Close health plan contract (Spring 2026)
- Prove model scales across 2-3 customers
- Build pipeline for Series A
Summary
Daisy AI
- Healthcare back-office is broken — we understand why
- AI + expert services, not just software
- First product: inpatient status decision support
- Real traction: major health plan contracting
- Team bridges the gap that kills healthcare startups
- Raising $2-3M to scale
We're building the company that health plans wish they could build internally.
Contact
Thomas Startz thomas@daisyai.com
Michael Yuan michael@daisyai.com
New York, NY