Reflect Scientific, Inc (RSCF) Fair Value & Analysis
Healthcare · US · Market cap $3.9M
Fair value as of: Jun 26, 2026
Analysis
Reflect Scientific, Inc (RSCF) currently trades at $0.0400, while our model-based Fair Value estimate is $0.0400 — implying the stock looks roughly 0.0% undervalued today. We read business quality at 95/100 (high quality), in the Healthcare 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: medium) — always confirm before acting.
About the company
Reflect Scientific, Inc., through its subsidiaries, engages in the design, development, and sale of scientific equipment for the life science and manufacturing industries worldwide. The company offers Cryometrix ultra-low temperature and blast freezers, which are used to store various products, including blood and cancer vaccines. It also provides laboratory consumables and disposables, such as filtration and purification products, customized sample handling vials, electronic wiring assemblies, high temperature silicone, and graphite and vespel/graphite sealing components for use by original equipment manufacturers in the field of gas/liquid chromatography in the chemical analysis industries. In addition, the company offers solvent chillers and refrigerated transportation products; and UV detectors and vial products. It serves hospitals, diagnostic laboratories, pharmaceutical and biotech companies, universities, governments, and private sector research facilities, as well as cold c…
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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.