Sarine Technologies Ltd (SILLF) Fair Value & Analysis
Technology · US · Market cap $56.5M
Fair value as of: Jun 26, 2026
Analysis
Sarine Technologies Ltd (SILLF) currently trades at $0.1660, while our model-based Fair Value estimate is $0.0800 — implying the stock looks roughly 51.8% overvalued today. We read business quality at 95/100 (high quality), in the Technology 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
Sarine Technologies Ltd. develops advanced technologies for the modeling, analysis, evaluation, planning, processing, grading, and tracking of diamonds in India, Africa, Europe, the United States, Israel, and internationally. The company offers Advisor (Tenders) and Advisor, which are rough planning software; AutoScan Plus, an automatic high-throughput marvel; Best Value, a software add-on; DiaExpert, a rough diamond planning system; DiaExpert Atom, a rough planning and marking system; DiaExpert Edge, a high-precision 3D modeling technology; DiaExpert Eye; DiaExpert Nano, a rough planning and marking system designed for smaller rough stones; DiaMark HD, DiaMark Z, and DiaMark Light Edition, which are laser marking systems; Diamension AXIOM, a diamond scanner for measuring and modeling polished diamonds; Diamension HD for accurate geometrical measurements of polished and semi-polished diamonds; DiaMobile XL, a compact and mobile system for evaluating and planning large rough diamonds…
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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.