Investor FAQ
Common questions, ready answers.
Company & Product
What does Daisy AI do?
We provide AI-powered services for health plan operations, starting with utilization management. Think: AI-augmented expertise, not just software.
How does the product work?
We're AI-native. Vibe coding lets us build whatever customers need, fast. We start from first principles every time — work with customers to customize for their workflows.
The form varies: could be a UI, could be backend automation, could be workflow tooling. The customer's needs determine what we build.
What's the first product?
Inpatient Status Assessment — decision support for the inpatient vs. observation determination.
When a patient is admitted, the status decision happens at T+4-8 hours. But no one applies criteria at that moment. Physicians decide without Two-Midnight guidance. Case managers review 14-24 hours later, after the decision is locked.
Our tool brings criteria-based reasoning to the decision point. Pulls clinical data automatically (vitals, labs, diagnosis), applies Two-Midnight reasoning, produces a structured assessment showing what supports the status, what's missing, and what to document.
Why this wedge: Concrete pain ($2K-$5K revenue difference per case), tractable problem (Two-Midnight provides a specific question AI can answer), differentiable from incumbents (not InterQual/MCG — automated, prospective, transparent reasoning).
Who is the customer?
Health plans. We start with utilization management because it's a clear pain point with quantifiable value. Premera is contracting now; pipeline includes AppriseMD, HURC, McBee, others.
Market
How big is the market?
Healthcare back-office is massive. Utilization management alone is a multi-billion dollar function across payers. The broader thesis: AI + human expertise will restructure how healthcare administrative work gets done. This isn't a feature — it's a platform shift.
Why now?
AI capability crossed a threshold. LLMs can now handle judgment-intensive tasks that were previously human-only. But you need domain expertise to apply them correctly. Most don't have that. The window is now — first movers who get this right will define the category.
Who are your competitors?
Incumbents like Epic don't understand AI. AI startups don't understand healthcare. We're rare: we bridge both. Most competitors fail because they don't understand the real problems — money doesn't fix that.
Business
What's the business model?
AI + expert services for health plans. We do the McKinsey work (strategy, problem identification) and the Palantir work (building software, running parts of the business).
Services vs. SaaS?
This is a red herring at N=1. Here's the real question: If 80% of healthcare organizations will be AI-enabled, who does that work?
Services come first: identifying problems, consulting, building custom solutions. Software emerges when you create repeatable processes. You can't have repeatable abstractions at N=1.
What you need: tight integration between business and technology. Not siloed thinking. Founders who hold both in the same mind. That's us.
What's the pricing?
Value-based. Platform at $250/seat/month. Discovery sprints at $15-25K. Implementation at $75-200K. Ongoing partnership at $20-30K/month. We solve expensive problems — clear ROI.
What's the sales motion?
Enterprise sales to health plans. Months, not weeks. But once you're in, you're in. Healthcare cycles are long but sticky.
Traction
What traction do you have?
Premera (major health plan) is contracting — close Spring. AppriseMD late stage. Pipeline building with HURC, McBee, SFUR, Westview Capital. Real engagement, not just conversations.
What's working? What's not?
Working: Health plans are engaging. The utilization management angle resonates. The "AI + expert services" model makes sense to buyers.
Learning: Healthcare moves slow. Sales cycles are long. But movement is real.
Team
Who are the founders?
Two founders with complementary backgrounds:
- Princeton engineering + Warburg Pincus healthcare PE — bridges business and technology
- Healthcare investing + technical execution — creative, culturally fluent, ships production software
Both can build. That matters.
Why are you the right team?
We bridge the gap that kills healthcare startups. Tech people don't understand healthcare economics. Healthcare people don't understand technology. We have both. And we can both build.
Both founders are technical?
Yes. One came from engineering; the other developed technical fluency through the partnership. Both now ship production software daily.
This is a feature, not a bug. We're in the vibe coding era. AI-native development changes what's possible. If we can develop this capability within our own team, we can do it for customers. That's the flywheel: we teach people to build, not just use. The bottleneck for AI transformation isn't models — it's talent. We're proof the talent gap can be closed.
Is two founders enough?
For this stage, yes — and it's a feature, not a constraint.
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. Coding in 2025 is different: work with AI, pick the right platforms, ship. We're not under-resourced. We're appropriately-resourced for the era.
This is also the thesis for customers: if we can do this with a small team, we can help them do the same. The leverage is real.
Fundraise
How much are you raising?
[Amount at terms — to be filled]
What will the funds be used for?
Expand team. Accelerate customer acquisition. Extend runway through first major closes.
What are the key milestones for this round?
Close Premera and AppriseMD (Spring). Prove the model scales. Build pipeline for next stage.
Technical
What's the technical moat?
The moat isn't the model — it's understanding where AI helps vs. creates liability. That requires healthcare depth. Our combination of healthcare PE and engineering is rare and not easily replicated.
How do you think about AI accuracy/reliability?
It matters a lot. Healthcare tolerates less error. Our approach: augment humans, don't replace them. AI handles grunt work; humans make judgment calls. That's how you build trust.
What's the data story?
[To be developed based on specific technical architecture]