Maintel Holdings (MAI) Fair Value & Analysis
Communication Services · GB · Market cap 13.3M GBX
Fair value as of: Jun 24, 2026
Analysis
Maintel Holdings (MAI) currently trades at p0.9250, while our model-based Fair Value estimate is p0.9500 — implying the stock looks roughly 2.7% undervalued today. We read business quality at 92/100 (high quality), in the Communication Services 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: high) — always confirm before acting.
About the company
Maintel Holdings Plc, together with its subsidiaries, provides communications managed services for the public and private sectors in the United Kingdom, European Union, and internationally. It operates through three segments: Project and On-Premises Managed Services, Network Services, and Mobile Services. The company offers unified communications and collaboration solutions, including UC Private Cloud +, UC Private Cloud, RingCentral, Teams Connector, Managed Mobile, UC Analytics, and SIP Trunking, Zoom Collaboration, and Zoom Phone. It also provides customer experience solutions, such as CX Private, Avaya Experience Platform, AudioSafe, Genesys Cloud, Gamma Horizon, and Zoom Contact Centre. In addition, the company offers security and connectivity solutions comprising SD-WAN, Security Service Edge, LAN, Cyber Services, and Secure Connect. Further, it provides managed services; and mobile fleet management and device management services. The company was founded in 1991 and is headqua…
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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.