SS Innovations International, Inc (SSII) Fair Value & Analysis
Healthcare · US · Market cap $694M
Fair value as of: Jun 24, 2026
Analysis
SS Innovations International, Inc (SSII) currently trades at $3.40, while our model-based Fair Value estimate is $0.8600 — implying the stock looks roughly 74.7% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare sector. Bear case: priced above our estimate, the market already discounts strong expectations. Bull case: above-average quality can justify a premium — the entry price still matters most (evidence: low).
About the company
SS Innovations International, Inc., together with its subsidiaries, operates as a commercial-stage surgical robotics company in India, Philippines, Indonesia, South America, Iraq, Cyprus, Oman, United Arab Emirates (UAE), Nepal, and internationally. It offers SSi Mantra, a surgical robotic system; and SSi Mudra, a range of stapling, energy, and core instrumentations for surgical systems. The company also develops NADI " Automated Coronary Anastomotic Connector, a micro stapling device; SSi Multi-Fire Clip Applier, a cartridge-based clip applicator; SSi Robotic Stapler; SSi Maya is an enabling digital platform that compliments the SSI Mantra system and enhances surgical capabilities; SSi Guru is a virtual tele illustration; SSi XR Cognitive Skill Simulator is an extended reality (XR) software platform developed to train and educate surgeons and surgical assistants; SSi MantraM; SSi Mantra Tele Surgeon Console (TSC); and 5mm surgical instruments; SSi Holographic Anatomy, an augmented …
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How we calculate Fair Value
Each company is valued through a stack of independent intrinsic-value models (DCF variants, residual-income, multiples and more), blended into one family-balanced consensus and weighted by how much trustworthy data backs it. A separate quality layer scores the fundamentals. Every input is real reported data — nothing guessed.
Educational research only · not financial advice · no buy/sell recommendation. Model-based estimates are not certainties; their reliability depends on data quality and assumptions.