Pre-seed · Confidential

Rōvn

The operating network for the healthcare workforce.

Workers verify once and reuse it across every job, shift, and facility. Facilities tap one network of already-verified workers instead of re-checking from scratch. Every workflow makes the network smarter.

How the network does the work: an AI Operator compresses hiring, credentialing, readiness, and monitoring, and trains the Rōvn Workforce Model. Built on verified trust, toward Indeed and LinkedIn scale.

$2.25M on a $15M post-money SAFE · AtlantaRōvn2026

The Problem

Healthcare rebuilds workforce trust from zero. Every time.

The same worker is re-verified at every facility, every renewal, every audit, in parallel silos.
A direct-hire RN takes about 78 days to become credentialed and billable.
For three months they sit idle and largely unpaid; the facility backfills with agency at a premium.
Miss one renewal and the facility is exposed at survey, or billing for an improperly-credentialed provider, which stays exposed to the False Claims Act under Medicare's 60-Day Rule.

Trust never compounds. The system pays full price for the same trust, over and over.

The ProblemRōvn02

The Insight

It is not a staffing problem.
It is a trust problem.

We started with people. We thought it was staffing. We were wrong. The people exist and the demand exists. What is broken is the machinery that moves a worker from interested to working, and trust is recreated instead of reused.

The fix: one operator that compresses the workflow, and makes trust reusable, worker-owned, and compounding.

The InsightRōvn03

One System

One system, not a pile of products.

Network + Demand Graph→ Worker Passport→ AI Operator→ Facility Intelligence→ Rōvn Workforce Model

Network + Demand Graph: verified workers meet real facility demand. The front door and distribution.
Worker Passport: workers prove trust once and reuse it across jobs and shifts.
AI Operator: the paid engine that compresses the workflow from applicant to working.
Facility Intelligence: each facility's accreting memory. The switching cost that deepens.
Rōvn Workforce Model: the system learns from every workflow. The moat.

The network is distribution. Operator is revenue. Model is defensibility. The end state is the operating network for the healthcare workforce.

One SystemRōvn04

The Trust Layer · Two Buyers

The Worker Passport

A worker-owned, source-receipted record: licenses, compliance, approvals, deployments, outcomes.
Prove once, reuse everywhere. The record travels with the worker across a career.
The worker owns the trust. Facilities consume it.
One-time document effort becomes a permanent, reusable asset.

For the facility

Hire verified workers, clear them faster, deploy them sooner.

For the worker

Verify once, get cleared faster, unlock more work.

The worker is the network. The facility is the customer.

Worker PassportRōvn05

The Memory Layer

Facility Intelligence

A Facility Agent represents each facility and learns how it actually operates.
Hiring, approval, staffing, and scheduling behavior, captured as the workflow runs.
It compresses the work and surfaces what needs attention. It never decides; a human decides at every gate.
Single-tenant memory that does not leave when a coordinator quits.

Switching cost that deepens the longer Rōvn runs the facility.

Facility IntelligenceRōvn06

The Moat

The Rōvn Workforce Model

Workers→ Passports→ Credentials→ Facilities→ Readiness→ Deployments→ Outcomes→ Retention→ Rōvn Workforce Model

The graph is the substrate that trains the Model. Value is created at every edge, not just the endpoints. A verified credential removes cost downstream. A deployment plus its outcome is proprietary data no competitor can assemble after the fact.

It begins as a graph plus frontier LLMs plus agents, and becomes proprietary as operated data accrues. Not a finished model, and not a fight with OpenAI. A silo cannot do this, because its data never leaves the building.

Rōvn Workforce ModelRōvn07

What We Run Today · Pre-launch

The rails are live.

The AI workforce operator: intake, source verification, credentialing, privileging, monitoring, audit.
36 source authorities: 25 state boards + NPDB, DEA, Nursys, OIG, SAM.gov, Verifiable, 5 payer adapters.
Receipt-bound trust enforced at the database: "verified" is un-writable without a source receipt.
Truth ladder + hash-chained audit, 7-year S3 Object Lock. 43 roles, 51 jurisdictions, 2,193 cells, 0 unsupported.
FastAPI on AWS ECS; Claude under a BAA. HIPAA-aligned, BAA available.

Zero paying customers. Pre-launch by design. The hard part is the architecture, and it is built.

What We BuiltRōvn08

The Operator

The Operator is named work, not a chatbot.

applicant→ screened→ documents→ source verified→ credentialed→ ready→ scheduled→ deployed→ monitored

Recruiting

Engages and screens applicants against role requirements.

Credentialing

Collects documents, extracts fields, builds the packet.

PSV

Checks source systems, captures a timestamped receipt.

Readiness

Answers start-ready, practice-ready, billable-ready.

Monitoring

Tracks expirations, sanctions, exclusions, renewals.

Demand

Captures open roles, shifts, urgency, coverage gaps.

Deployment

Recommends workers who are ready, available, approved; the facility decides.

Facility

Learns each facility's rules, tendencies, bottlenecks.

Eight named agents on one spine. Rōvn operates the workflow. Humans make every credentialing, privileging, hiring, scheduling, and deployment decision.

The OperatorRōvn09

The Trust Spine

Verified means verified.

Receipt-bound

Every trust state references a valid source receipt, enforced by a database trigger. Not a code convention, a rule.

Hash-chained audit

Every action writes a receipt; each links to the prior. Replayable on demand for survey and recoupment defense.

Named-human decisions

Four gates: interview, offer, hire, privilege. The AI prepares everything and waits. It never decides.

Rōvn does the work. Source systems prove the facts. Humans make every regulated decision.

Trust SpineRōvn10

The Intelligence Ladder

The graph trains the Model. Not the reverse.

PHASE 1AI Workers: credentialing, compliance, monitoring, readiness (live today)

PHASE 2ML Models: readiness, facility-fit, matching, routing, retention

PHASE 3Workforce Intelligence + Forecasting: shortages, demand, bottlenecks, staffing risk

PHASE 4Rōvn Workforce Model: graph plus frontier LLMs plus agents, proprietary as operated data accrues

Graph-first. Today it is a system, not a finished base model, and the fight is not with OpenAI. A competitor with a better model still has an empty graph. Matching and forecasting stay firewalled from the hiring gates.

AI WorkersRōvn11

Why It Compounds

Rōvn gets stronger every time a worker, facility, credential, deployment, and outcome enters.

Worker Passport flywheelWorker joins → Passport gets more verified → more facilities trust it → more deployments → more outcomes → more workers join.

Facility flywheelFacility joins → workflow learned → its agent sharpens → more value → more facilities, each arriving with its roster.

Workforce Model flywheelEvery workflow creates data → the Rōvn Workforce Model improves → matching and deployment improve → network value rises → more workflows enter.

Pre-launch honesty: the engine is built, not yet a flywheel in motion. The seed round turns it.

Why Network CompoundsRōvn12

The Billion-Dollar Path

Why this does not stop at credentialing.

Rōvn sits at the intersection of workforce software, labor marketplaces, credentialing and provider data, healthcare AI infrastructure, and compliance. That intersection creates several credible exit paths.

Workforce software

A workforce-management buyer (the category around QGenda, symplr, UKG, Workday) acquires Rōvn to own verified trust, readiness, and deployment intelligence. QGenda was publicly reported sold in the $2-3B range.

Healthcare AI / data

A healthcare AI or data platform acquires Rōvn for the workforce graph, verified credentials, demand signals, and facility workflow data. Innovaccer was publicly reported a healthcare-AI unicorn at $3.45B in 2025.

Staffing or payer

A staffing platform acquires the verified readiness and deployment layer above placement; or a payer or provider network acquires Rōvn to cut credentialing, enrollment, and billing-readiness friction.

The highest-value outcome is no acquisition at all: Rōvn becomes the standalone healthcare labor network. We do not put probabilities in front of investors; reference points above are publicly reported, not promises.

Why This Becomes LargeRōvn13

Market

The largest workforce in the country, and it cannot staff itself.

~18M

healthcare workers; 31% of all 2024 US job growth (BLS)

$16-18B

summable software floor we collapse into one platform

$30-50B

workforce trust-and-intelligence layer we build toward

$1.2B / yr

payer re-credentialing duplication, priced by Medallion

$496B

US billing-and-insurance admin, ~80% excess (CAP)

up to 86,000

projected physician shortfall by 2036 (AAMC; range 13,500 to 86,000)

MarketRōvn14

Why Now

The regulator just moved the market to our architecture.

NCQA mandates ongoing monitoring at least every 30 days (effective July 2025).
Joint Commission demands proven primary-source verification, not a claim. The unit of truth is a receipt.
CMS Billing for an improperly-credentialed provider remains exposed to False Claims Act liability under Medicare's 60-Day Rule.
BAA-grade AI over credentialing documents is finally cheap enough for a mid-size facility, a 2025 capability.

Receipts and continuous monitoring stop being optional. The reaction window is 2026 to 2028. Rōvn is built native for it.

Why NowRōvn15

Competitive

Everyone owns a different layer. Rōvn owns trust.

symplr · Verisysoperate the workflow, locked to facility silos

Rōvnoperate the workflow + worker-owned, source-receipted evidence

CAQH · ATS / HRISattestation, payer-owned (Jan 2026); not an operator

certifyOS · VerifiableAPI verification, per-customer, not full operation

Worker-owned reusable evidence →

Operates the workflow →

LinkedIn owns identity. Indeed owns discovery. Neither verifies trust.
Staffing owns placement, and the data dies in its silo. Rōvn takes no placement fees, ever.
CAQH went payer-owned in January 2026. We are owned by no payer; the worker owns the record.
The open corner of the map is unoccupied. We sit in it.

Why Incumbents LoseRōvn16

Go To Market

Provider groups first. Ten paid pilots. One corridor.

Provider groups before hospitals: shorter sales cycles, and each arrives with its clinicians attached.
A paid entry wedge: Readiness at $2,500/mo, then the 90-day Operator pilot. Paid, never free.
Aki Hashmi (GTM advisor) is vetting and opening the relationships.
The right ten become the first corridor where the network starts to compound.

Facility selection is not a formality. It is how we earn the proof the seed round buys.

Design PartnersRōvn17

Honest State

We do not fake traction. We show the rails.

WHAT IS REAL

36 source authorities live; receipt-bound trust; 2,193 coverage cells.
MVP and Phase 1 shipped, backend and frontend.
Pilot pipeline building; advisor bench in place.

WHAT IS NOT

Zero paying customers. Pre-launch by design.
SOC 2 and pentest in progress, not certified.
Most of the autonomous-operator surface is roadmap.

The risk is execution, not pretending there is traction. That honesty is the slide investors trust.

TractionRōvn18

Team

Built by operators who live the problem.

Giles-Evan Mboumi

Founder & CEO. Designed and deployed Rōvn's verification architecture. 3 yrs healthcare sales (Boehringer Ingelheim); built a 200+ customer business from zero. Owns vision, fundraising, GTM, pilots, recruiting.

Christian Montgomery

Co-Founder & COO. Built the deployed attestation pipeline as founding engineer. B.S. Cybersecurity (Kennesaw State, 3.96). Owns operations, finance, delivery, security and infrastructure.

Abhishek Jha

CTO. ~6 yrs SWE (Accenture; Senior SWE at Eruvaka); rebuilt a healthcare data pipeline from over a week to ~90 min across 15M+ records. Owns architecture, implementation, infra, reliability.

Gokul Shanmugam

CPO. Engineer at Aetna, then 3.5 yrs building EHR software at Athenahealth, plus RCM automation at Commure. Owns product, roadmap, verification-engine behavior.

Advisors: Dr. Danielle K. Miller, DNP RN (Clinical Advisor, former CNO + ex-Amazon Principal PM) · Dr. Mohammed Quadri, MD MBA (healthcare-AI) · Aki Hashmi (GTM, CEO SkinSAFE). Four founders on 4-year vesting; completed Delaware C-corp; outside counsel engaged.

TeamRōvn19

The Raise

$2.25M on a $15M post-money SAFE.

Commercialize the Operator from first paid pilots to recurring contracts.
Compliance and security hardening: SOC 2, pentest, source-access expansion.
Repeatable GTM in the first Atlanta corridor, provider groups first.

Pre-seed. The round buys proof of density in the first corridor, the thing that turns the engine into a flywheel.

FundraiseRōvn20

Roadmap

Wedge to utility.

Readinessthe live wedge, today

Operatorhiring, credentialing, privileging, payer, monitoring, audit

Worker Passport + Networkworkers own and reuse verified evidence

Workforce Intelligence + Forecastingthe labor-market data layer

Workforce Operating Systemscheduling, deployment, optimization

National Workforce Utilitythe rail everyone plugs into

RoadmapRōvn21

The Vision

Trust should compound.

LinkedIn owns professional identity. Indeed owns job discovery. Rōvn owns verified workforce trust.

Rōvn is the operating network for the healthcare workforce. How the network does the work: an AI Operator compresses the workflow that takes a worker from applicant to working. As facilities and workers join, that network becomes a new layer of healthcare infrastructure, built on verified trust, at Indeed and LinkedIn scale.

rovn.to · investors.rovn.toRōvn22