Nanalysis Scientific Corp (NSCIF) Fair Value & Analysis
Technology · US · Market cap $11.5M
Fair value as of: Jun 26, 2026
Analysis
Nanalysis Scientific Corp (NSCIF) currently trades at $0.0831, while our model-based Fair Value estimate is $0.1802 — implying the stock looks roughly 116.9% undervalued today. We read business quality at 95/100 (high quality), in the Technology 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
Nanalysis Scientific Corp. develops, manufactures, and sells magnetic resonance technology products in Canada, the United States, Europe, Asia, and internationally. The company operates through the Scientific Equipment and Security Services segment. It offers nuclear magnetic resonance (NMR) spectrometers; magnetic resonance test, and measurement and diagnostic system; a 60-NMR platform; 100-NMR platform; 60-TEACH; QUAD NMR Console; and the Cameleon 4 console, a compact alternative to facilitate MRI for teaching. The company also provides software products, such as NMRGui, a user interface for data acquisition and processing of benchtop NMR products; SPINit MR software platform to facilitate data acquisition, processing, and pulse programming; and NMRFx platform that offers a suite of software tools to streamline and automate MR data analysis and management. In addition, it offers security services, including preventive and on-call maintenance services, scheduled preventative mainte…
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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.