Equippp Social Impact Technologies Limited (EQUIPPP) Fair Value & Analysis
Technology · IN · Market cap ₹2.2B
Fair value as of: Jun 29, 2026
Analysis
Equippp Social Impact Technologies Limited (EQUIPPP) currently trades at ₹21.65, while our model-based Fair Value estimate is ₹2.77 — implying the stock looks roughly 87.2% overvalued today. We read business quality at 82/100 (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
Equippp Social Impact Technologies Limited provides IT solutions and services in India. It enables organizations to capture the business benefits of emerging technologies of digital engineering, business intelligence, analytics, machine learning, testing, and IT consulting, as well as offers degree of skills, IPs, and domain expertise across in the areas of digital transformation, enterprise solutions, tech platforms for ESG, CSR, and public private partnership projects. The company also provides EQUIPPP, a digital approach that aims to enable cross-sector collaborations and facilitate the evolution of public-private partnerships; EQUIPPP IX, an information exchange platform that allows collection of feedback and insights from project beneficiaries across multiple geographical locations through digital media, CAPI, CATI, CAWI, and social value partners; and EquiPPP Connect, a launchpad cum business center for Indian origin next gen tech entrepreneurs. In addition, it offers IT staff…
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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.