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Sense Technologies, Inc (SNSGF) Fair Value & Analysis

Consumer Cyclical · US · Market cap $2.0K

ST Sense Technologies, Inc logo Sense Technologies, Inc SNSGF · US
Price$0.0001
Fair Value$0.0001
Upside+0.0%
Quality36/100
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Evidence: Low Range $0.0001 – $0.0001

Fair value as of: Jun 26, 2026

From 4 valuation models · updated 7 days ago

Fair value updated Jun 26, 2026 — revised from $0.0300 to $0.0001 (−99.7%) since Jun 24, 2026.

Price vs Fair Value (12 months)

$0.0001 $0.0001 Fair Value $0.0001 Jul 2025 Jul 2026

12‑month range $0.0001 – $0.0001 · the $0.0001 price screens below the $0.0001 fair value. As of Jun 26, 2026.

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Analysis

Sense Technologies, Inc (SNSGF) currently trades at $0.0001, while our model-based Fair Value estimate is $0.0001 — implying the stock looks roughly 0.0% undervalued today. We read business quality at 36/100 (below-average quality), in the Consumer Cyclical 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.

Trailing-twelve-month revenue stands at $479K. Revenue grew 213.9% year over year. Fundamentals as of Jun 26, 2026

Key figures & financial health

Revenue (TTM) $479K
Revenue growth (YoY) +214%
Net margin -275%
Operating margin -14.8%

Figures from reported company fundamentals (EODHD) · as of Jun 26, 2026. TTM = trailing twelve months.

About the company

Sense Technologies, Inc. develops and markets automotive safety devices that enhance driver awareness of people or obstacles located in vehicle blind spots in the United States. The company offers Guardian Alert Doppler awareness system, which warns vehicle drivers of the presence of people or obstacles in blind spots that exist behind their vehicles when backing up. It markets its Guardian Alert product primarily to automobile and truck dealers, fleet operators, and other after-market automotive industry participants. The company also offers ScopeOut, a system of specially designed mirrors that are placed at specific points on automobiles, trucks, sport utility vehicles, or commercial vehicles to provide drivers a complete view behind the vehicle. It markets its ScopeOut product to department stores and other retailers as an after-market automotive safety product, as well as sells online at sensetech.com. The company was formerly known as Graham Gold Mining Corporation and changed …

Revenue & earnings trend

FY2013 – FY2021 · reported fiscal years

Sense Technologies, Inc reported revenue of $190K in FY2021 versus $299K in FY2013, a compound −5.5%/yr. Reported net income was −$993K in FY2021.

Revenue −5.5%/yr
FY13 $299K
FY14 $639K
FY15 $149K
FY16 $318K
FY21 $190K
Net income
FY13 −$569K
FY14 −$753K
FY15 −$746K
FY16 −$572K
FY21 −$993K

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Frequently asked questions

Is Sense Technologies, Inc (SNSGF) undervalued?
As of Jun 26, 2026, our model estimates a fair value of $0.0001 versus a price of $0.0001 — about +0% (undervalued). Model-based estimate, not financial advice.
What is the fair value of SNSGF?
Our model-based fair value for Sense Technologies, Inc is $0.0001 (as of Jun 26, 2026), built from audited fundamentals. The current price is $0.0001.
What is the quality score of SNSGF?
Sense Technologies, Inc has a Quality Score of 36/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.
What is the revenue of Sense Technologies, Inc (SNSGF)?
Sense Technologies, Inc reported trailing-twelve-month revenue of about $479K (latest available figure, as of Jun 26, 2026).

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.