Datamatics Global Services Limited (DATAMATICS) Fair Value & Analysis
Technology · IN · Market cap ₹47.2B
Fair value as of: Jun 29, 2026
Analysis
Datamatics Global Services Limited (DATAMATICS) currently trades at ₹799.80, while our model-based Fair Value estimate is ₹657.15 — implying the stock looks roughly 17.8% overvalued today. We read business quality at 96/100 (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: high).
About the company
Datamatics Global Services Limited engages in the provision of intelligent solutions across digital technology solutions, business process management, and engineering services in India, the United States, the United Kingdom, Europe, and internationally. The company operates through three segments: Digital Operations, Digital Technologies, and Digital Experiences. It offers TruBot, a robotic processing automation solution; TruCap+, an AI-enabled data capture solution; TruBI, a business intelligence and data visualization solution; TrueAI, an artificial intelligence and cognitive sciences platform; TruFare, an automated fare collection solution; Datamatics Digital Workplace Solution offers cloud-based, paperless digital e-office solutions; TruDiscovery, the enterprise search solution; and Datamatics FINATO, a digital platform for end-to-end CFO back-office automation. It serves banking and finance, credit rating, healthcare, transportation, insurance, manufacturing and logistics, trav…
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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.