BIPROGY Inc (NTULF) Fair Value & Analysis
Technology · US · Market cap $2.8B
Analysis
BIPROGY Inc (NTULF) currently trades at $28.78, while our model-based Fair Value estimate is $48.47 — implying the stock looks roughly 68.4% 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: high) — always confirm before acting.
About the company
BIPROGY Inc. provides cloud computing and outsourcing services in Japan. It also engages in the sale/rental of computer and network system; development and sale of software; and provision of system related services. In addition, the company provides digital transformation, workstyle reforms, artificial intelligence, robot deployment, blockchain, IoT, service design, agile development and support, DevSecOps framework, environment and energy, travel and inbound tourism, BCP, disaster measure, remote monitoring, security, marketing, cashless payment, customer experience management, customer relationship management, and data usage services. Further, the company offers SRM, electronic procurement and purchasing, logistics, enterprise resource planning, workplace/facility management, storage service platform, and corporate training services. Additionally, it provides cloud services, IoT, system platform, web development platform, O and M, dev support, integration platform, next generation…
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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.