TurnOnGreen, Inc (TOGI) Fair Value & Analysis
Industrials · US · Market cap $4.7M
Fair value as of: Jun 26, 2026
Analysis
TurnOnGreen, Inc (TOGI) currently trades at $0.0258, while our model-based Fair Value estimate is $0.0257 — implying the stock looks roughly 0.3% overvalued today. We read business quality at 80/100 (high quality), in the Industrials 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
TurnOnGreen, Inc, through its subsidiaries, designs, develops, manufactures, and sells power conversion systems for mission-critical applications. It provides EV700 (SMUD), a non-networked electric vehicle (EV) charger with custom configuration capabilities; EV700 (LADWP), a charger designed for your home charging needs; Level 2 electric vehicle chargers for commercial charging; and DC Fast Charger, a user-friendly charger management system that allows for real-time monitoring, rate setting, remote operations, and energy reporting of EV charging sessions, as well as TurnOnGreen Network, a services allow you to connect and control your entire EV infrastructure in one place. The company also offers retrofit and software support solutions for shell recharge and volta EV chargers, and Microgrid-Integrated EV Charging that operates in conjunction with the main power grid. It serves EV charging, healthcare, defense and aerospace, telecom and network, and transportation industries. The com…
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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.