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GoGo AI Network Inc (GOGAF) Fair Value & Analysis

Financial Services · US · Market cap $12.4M

Price$0.1350
Fair Value$0.0500
Upside-63.0%
Quality91/100
Evidence: Medium Range $0.0400 – $0.0600

Fair value as of: Jun 26, 2026

Analysis

GoGo AI Network Inc (GOGAF) currently trades at $0.1350, while our model-based Fair Value estimate is $0.0500 — implying the stock looks roughly 63.0% overvalued today. We read business quality at 91/100 (high quality), in the Financial Services 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: medium).

About the company

GoGo AI Network Inc. invests in healthcare technology companies. The company was formerly known as MedBright AI Investments Inc. and changed its name to GoGo AI Network Inc. in January 2026. GoGo AI Network Inc. is headquartered in Vancouver, Canada.

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Frequently asked questions

Is GoGo AI Network Inc (GOGAF) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0500 versus a price of $0.1350 — about −63% (overvalued). Model-based estimate, not financial advice.
What is the fair value of GOGAF?
Our 21-model fair value for GoGo AI Network Inc is $0.0500 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.1350.
What is the quality score of GOGAF?
GoGo AI Network Inc has a Quality Score of 91/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.