Sangoma Technologies Corporation (SANG) Fair Value & Analysis
Technology · US · Market cap $120M
Fair value as of: Jun 25, 2026
Analysis
Sangoma Technologies Corporation (SANG) currently trades at $3.72, while our model-based Fair Value estimate is $15.64 — implying the stock looks roughly 320.4% 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
Sangoma Technologies Corporation, together with its subsidiaries, develops, manufactures, distributes, and supports voice and data connectivity components for software-based communication applications in the United States of America and internationally. The company offers communications platforms comprising pure cloud and hybrid unified communications as a service, and on-premises systems; retail and wholesale SIP trunking, as well as fax as a service; Sangoma TeamHub, a unified communications and collaboration platform for business productivity; Sangoma Meet, a multi-party video conferencing platform; and Sangoma CX, a cloud-native contact center suite that enables businesses to manage inbound interactions across multiple channels. It also provides productivity apps comprising appointment suite, call flow, curbside, phone monitor, send, and urgent notify; managed security services, such as antispam, web filtering, antivirus, botnet and domain reputation, app control and intrusion p…
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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.