Conversational Pitches
For hallway conversations, not stage presentations. Natural, not rehearsed.
30 Seconds — "What do you do?"
We do forward deployed engineering for health plans. Think Palantir's model but for healthcare operations — we embed with payers, understand their utilization management workflows, and build AI tooling that actually fits how their teams work. We're closing our first health plan client now, about $25k a month, and looking for the next two or three.
2 Minutes — "Tell me more"
So the core insight is that healthcare AI keeps failing because the people building it don't understand the operations. You can't just throw a model at utilization management — you need to understand why a nurse makes a particular determination, what criteria she's actually using versus what the policy says, how information flows between the plan and the provider.
We come from healthcare PE, so we've seen this from the investment side — what works, what doesn't, why most healthtech vendors fail to get adoption. And we're engineers. So we do what Palantir does: we go in, sit with the team, map the real workflows, and build production AI tools on top of what we find.
We're about to start with Premera — that's our first health plan engagement. It's utilization management, which is this massive, manual, judgment-intensive process that's perfect for AI augmentation. Not automation — augmentation. The nurses still make decisions, but they're faster and more consistent.
The model scales because every engagement feeds back into our platform. What we build for Premera becomes the foundation for the next client.
5 Minutes — "I'm interested, keep going"
Build on the 2-minute version, then add:
The market timing is important. Two things happened at once: AI crossed a capability threshold for judgment-intensive work — these models can now read a clinical chart and reason about medical necessity in a way that wasn't possible two years ago. And health plans are under enormous pressure. CMS is tightening rules on prior authorization, staffing costs are rising, and the volume of reviews keeps growing. They need help, and their current vendors are selling them software that doesn't get adopted.
That's the gap we fill. We're not a SaaS vendor who ships a login and wishes you luck. We embed. We understand your specific workflows, your specific criteria sets — whether you're using MCG or InterQual — your specific EHR integrations. And we build tools that work in your environment, not a demo environment.
The business model: we start with a services engagement — discovery, workflow mapping, prototype delivery. That turns into a recurring platform fee as the tools prove value. Premera is starting at about $25k a month. We think the right health plan engagement is $200-500k annually once expanded.
On pipeline: we've identified over 100 health plans in the US that are the right size and complexity for this model. The next step after Premera is landing two or three more in the next few months to prove repeatability.
For fundraising context: we've talked to Battery, Primary, NextGen, Norwest, and several others. The story resonates with investors who understand healthcare — the ones who've seen how many AI companies fail at implementation. We're raising to accelerate hiring and pipeline development.
What makes us hard to replicate: it's the combination. Healthcare PE understanding of incentives and economics, plus engineering capability to actually build, plus the FDE model that creates switching costs through deep integration. Most teams have one of those. We have all three.
Audience Adjustments
To a payer exec: Skip the investor framing. Lead with "we help health plans modernize utilization management" and focus on the pain — staffing, consistency, CMS pressure. Ask about their current UR process.
To an investor: Lead with the Palantir comparison and market size. Emphasize Premera as validation. Be honest about stage — pre-revenue but contracting.
To another founder: Be real. "We're two people, closing our first health plan client, figuring out FDE in healthcare. Learning a ton." Founders respect honesty about stage.
To a tech person: Go deeper on the AI. "We're using Claude with structured extraction on clinical documents, matching against MCG/InterQual criteria sets, building decision support into existing UR workflows." They'll ask good technical questions — let that conversation happen.