One founder, one path, every output. This is exactly what Persoona generates from a single LinkedIn input. No signup needed to read through it. When you're ready, the same flow runs on your background.
In this example
What the founder gives Persoona
Input
LinkedIn URL: linkedin.com/in/maya-chen
Pasted profile text
“Maya led billing operations at a mid-market hospital system for the last twelve years. She managed denials, automation rollouts, payer negotiations, and a team of eighteen.”
What Persoona extracts
Extracted
What gets published
Maya Chen
Operator-Domain-Expert
Twelve years inside healthcare RCM. Building the autonomous billing copilot that the next generation of revenue teams wish existed.
Unfair advantage
Public at persoona.ai/p/maya-chen
Indexable on day oneThree ideas grounded in the advantage
Idea
ChosenAn AI assistant that drafts the appeal narrative the moment a denial code lands, grounded in payer-specific precedent.
Idea
Comparative payer-behavior dashboard so revenue teams know which contracts are quietly underpaying.
Idea
A structured curriculum that turns mid-career billing operators into RCM directors.
The decision that locks the rest of the journey
Maya picks
Denial Copilot
From this point forward, every step builds on this single choice. Validation, intellectual property, product, growth, capital. The user makes decisions; the AI does the work in between.
Market check + intellectual property plan
Validation
Market size
US hospital billing AR is roughly $200B annually. Denials cost the industry $20B in re-work. Even 1% capture is a meaningful business.
Buyer urgency
Revenue cycle directors are evaluated quarterly on first-pass denial rate. They are paid to fix this. Budget exists.
Real risks
Incumbent EHR vendors will copy the feature. Defensibility comes from payer-specific precedent data Maya can build a moat around.
Intellectual property plan
Defensibility
The asset is the payer-specific appeal-precedent dataset. Maya's network is the moat for collecting it; speed of collection is the moat for keeping it.
Prior art search
Three patents in adjacent denial-prediction space. None overlap with the appeal-narrative-generation method Maya is filing on.
Filing plan
Provisional within 30 days. Full utility filing within 12 months. Trademark on Denial Copilot now.
Ship-ready scope
Landing page
denialcopilot.com. Headline: 'Draft the appeal the moment the denial lands.' One screenshot, one CTA, one proof line.
Product backlog
Auth, denial-code intake, payer-precedent retrieval, appeal-draft generation, copy-to-clipboard, audit log. Six items, eight weeks.
Launch checklist
Three pilot hospital systems from Maya's network, $0 contract, full data-share for the precedent dataset.
Want a real GitHub repo provisioned for this? Connect GitHub from your dashboard at any time. It is never a prerequisite for the steps before it.
Distribution + content + CAC
Distribution
Direct outbound to RCM directors via Maya's existing network. Followed by case studies and a quarterly RCM operator webinar.
Content
One newsletter a week breaking down a real payer denial pattern. Demonstrates the product without requiring it.
CAC and payback
Estimated CAC $4,200 per seat. Payback at four months. Six-figure ACV after the third seat per system.
Raise-ready, not raise-blocked
Round size
$1.5M pre-seed for a 12-month runway. Small team, high-conviction angels, two healthcare-savvy funds, no marquee logo needed.
Investor matches
Six healthcare-domain angels and two seed funds whose theses match denial automation specifically.
Pitch outline
Maya's twelve years inside the workflow + the specific payer-precedent moat + early letters of intent from three pilot systems.
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