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TaoWeave, Inc (TWAV) Fair Value & Analysis

Financial Services · US · Market cap $4.6M

Price$1.29
Fair Value$2.03
Upside+57.4%
Quality95/100
Evidence: Low Range $1.34 – $2.54

Fair value as of: Jun 26, 2026

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Analysis

TaoWeave, Inc (TWAV) currently trades at $1.29, while our model-based Fair Value estimate is $2.03 — implying the stock looks roughly 57.4% undervalued today. We read business quality at 95/100 (high quality), in the Financial Services sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.

About the company

TaoWeave, Inc. is a digital asset treasury company. The company focuses on Bittensor (TAO), a decentralized, open-source protocol that creates a marketplace for machine intelligence. It also enables AI models to be trained, shared, and monetized in a permissionless environment. In addition, it provides TAO a native token of the Bittensor network, used for staking, governance, and compensating AI model contributors. The company was formerly known as Oblong, Inc. and changed its name to TaoWeave, Inc. in December 2025. TaoWeave, Inc. is headquartered in Denver, Colorado.

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Frequently asked questions

Is TaoWeave, Inc (TWAV) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $2.03 versus a price of $1.29 — about +57% (undervalued). Model-based estimate, not financial advice.
What is the fair value of TWAV?
Our 21-model fair value for TaoWeave, Inc is $2.03 (as of Jun 26, 2026), built from audited fundamentals. The current price is $1.29.
What is the quality score of TWAV?
TaoWeave, Inc has a Quality Score of 95/100, 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.