# Osso VR Financial Model VR-based surgical training and objective assessment platform for surgeons and medical device companies. - Canonical: https://finamodel.com/startups/osso-vr - Excel download: https://finamodel.com/startup-models/osso-vr.xlsx - Category: Health-tech - Model type: SaaS ARR / Valuation - Funding round: Series C - Founded: 2022 - Geography: US-focused (named partners: Rush University Medical Center, Johns Hopkins, HSS); global aspiration implied by "1.1 million surgeons globally" [DECK, slide 4]. - Customer: B2B ## About the company Osso VR provides virtual-reality surgical training and objective assessment for surgeons and medical-device companies. Its platform gives clinicians immersive practice and performance feedback, while device companies can use specialised content to support training around their products and procedures. Medical-device firms and hospital or residency programmes create separate enterprise customer segments. Revenue can expand through content modules, trainees, procedure libraries, and deployments across institutions, while implementation and training adoption influence renewal and customer success. The model forecasts OEM contracts, institutional licences, content modules, trainees, implementation, renewal, and platform-delivery cost. It includes content production, hardware or VR support, enterprise sales, customer success, gross margin, product investment, cash burn, and runway. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition - VR headset-based surgical training platform (appears to use Oculus/Meta Quest hardware, branded as Osso VR). - Three core differentiators: hands-on simulation, collaborative multi-user training, and objective performance assessment via scored proficiency ratings. - Replaces or augments traditional education journey (product demo → cadaver lab → first case) with pre-training, qualification, refresher training, and team training touchpoints using VR. - Medical device company use case: VR-enabled sales training - surgeons pre-trained on a device before cadaver lab, improving adoption and shortening the sales cycle. ## Market - 1.1 million surgeons globally. - 310 million major surgeries per year. - $8.3 trillion global cost of healthcare - used as a broad context stat, not a directly addressable market figure. ## Revenue model - Revenue streams implied but not explicitly priced in the deck: - Medical device company channel: device companies pay for VR training content/platform to train surgeons on their products (sales enablement + adoption use case). - Hospital/residency program channel: institutions pay for surgical training access. - Hardware (headset) economics: not addressed - unclear if hardware is sold, leased, or provided by customers. ## Traction & metrics - 20+ hospital & residency programs. - 12+ medical device companies. - Named medical device partners: Smith & Nephew, Stryker, Medtronic, Johnson & Johnson, Endologix, Zimmer Biomet. - Named hospital/academic partners: Rush University Medical Center, Johns Hopkins University, Hospital for Special Surgery. - Clinical evidence: - Tibial shaft fixation RCT (N=20): Osso VR training 230% better performance scores vs. standard training; VR group 78% procedural completion vs. PDF group 25% (300% improvement) - published in *Journal of Surgical Education* Feb 2020 and *Clin Orthop Relat Res* (2020) 478:1-8. - Unicondylar knee arthroplasty (N=20): Osso VR total score 33 vs. PDF 27 (max 36); procedure time 26.73 min vs. 35.45 min; redirects 3 vs. 9; training time 38.82 min vs. 50 min. ## Competition / moat - No explicit competitive slide. - Implied moat: peer-reviewed clinical evidence of efficacy (published RCTs); existing relationships with top-6 medical device OEMs and elite hospital/academic programs; objective assessment data layer (proficiency scoring) is defensible as a proprietary dataset over time. - Traditional training methods (cadaver labs, PDF/didactic instruction) are the de facto alternative - no named VR competitors called out. ## Team & funding ask / use of funds --- ## Recommended financial model - **Archetype + why:** Dual-revenue B2B SaaS / platform model. - Revenue stream 1: Medical device company subscriptions/contracts (platform access + content modules per procedure/device). This is likely the primary and higher-value stream - OEMs are well-funded and have strong ROI incentive (faster surgeon adoption = more implant revenue). - Revenue stream 2: Hospital/residency program subscriptions (seat-based or institution-based). Lower ACV but demonstrates clinical validation and drives surgeon familiarity. - Model type: ARR-build with two cohorted customer segments, not a usage-based or transactional model given the B2B institutional nature. - **Forecast horizon & granularity:** 5-year annual model (Years 1–5), with Year 1 broken into quarterly detail. Given early commercial stage implied by the partner count (12+ OEMs, 20+ hospitals), monthly granularity for Year 1 is useful for cash planning. - **Key drivers & assumptions:** - Medical device company customers: - Starting customers: 12 - New logos per year: 4–8/year; rationale: early land-and-expand with top-6 majors already signed, mid-tier OEMs are the next wave - ACV per OEM: $150K–$500K/year; rationale: OEM training budgets are large; comparable medtech SaaS platforms price in this range - Expansion/upsell rate: 20% NRR uplift as OEMs add procedure modules; rationale: multi-procedure expansion is the natural motion once platform is deployed - Gross churn: 5–10% annually; rationale: sticky once integrated into sales training workflow - Hospital/residency program customers: - Starting customers: 20 - New logos per year: 10–20/year; rationale: large addressable base, but longer sales cycles and smaller budgets - ACV per institution: $20K–$75K/year; rationale: hospital tech budgets are constrained; per-seat pricing typical - Gross churn: 10–15%; rationale: budget sensitivity in hospital systems - Gross margin: 70–80%; rationale: software-heavy with content creation costs; hardware is customer-owned (Oculus Quest) - Content development cost: $50K–$150K per procedure module; rationale: medical-grade VR content is expensive to build and validate - S&M as % of revenue: 30–40% in early years declining to 20–25%; rationale: B2B enterprise sales motion with specialist reps - R&D as % of revenue: 20–30%; rationale: ongoing platform and content investment - G&A: 10–15% of revenue - **Scenarios (Base / Bull / Bear - which variables flex):** - Base: OEM adds 6/year, hospital adds 12/year, OEM ACV $250K, hospital ACV $40K, 75% gross margin - Bull: OEM adds 10/year (international expansion, smaller OEMs), upsell kicks in faster (30% NRR), ACV rises as platform matures - Bear: OEM sales cycle lengthens (regulatory/procurement), hospital budgets cut, content development costs exceed plan, hardware dependency on Meta creates distribution risk - **Required sheets / outputs:** 1. Assumptions dashboard (all drivers in one place, scenario toggle) 2. Revenue build - OEM segment (logos × ACV × expansion) 3. Revenue build - Hospital segment (logos × ACV) 4. P&L (Revenue, COGS, Gross Profit, S&M, R&D, G&A, EBITDA) 5. Headcount plan (sales reps, content/R&D, G&A) 6. Cash / runway (if funding ask is added) 7. KPI summary (ARR, logo count by segment, NRR, CAC, LTV, LTV:CAC) ## Frequently asked questions ### Is the Osso VR financial model free? Yes. The Osso VR model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.