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Ajooni Biotech Limited (AJOONI) Fair Value & Analysis

Consumer Defensive · IN · Market cap ₹637M

Price₹3.69
Fair Value₹4.27
Upside+15.7%
Quality97/100
Evidence: Medium Range ₹3.20 – ₹5.33

Fair value as of: Jun 29, 2026

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Analysis

Ajooni Biotech Limited (AJOONI) currently trades at ₹3.69, while our model-based Fair Value estimate is ₹4.27 — implying the stock looks roughly 15.7% undervalued today. We read business quality at 97/100 (high quality), in the Consumer Defensive 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: medium) — always confirm before acting.

About the company

Ajooni Biotech Limited manufactures and sells cattle feed in India. Its portfolio includes cattle feed chips, camel and pig feed, cotton oil cakes, mustard oil cakes, and feed supplements. The company was incorporated in 2010 and is based in Mohali, India.

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Frequently asked questions

Is Ajooni Biotech Limited (AJOONI) undervalued?
As of Jun 29, 2026, our model estimates a fair value of ₹4.27 versus a price of ₹3.69 — about +16% (undervalued). Model-based estimate, not financial advice.
What is the fair value of AJOONI?
Our 21-model fair value for Ajooni Biotech Limited is ₹4.27 (as of Jun 29, 2026), built from audited fundamentals. The current price is ₹3.69.
What is the quality score of AJOONI?
Ajooni Biotech Limited has a Quality Score of 97/100, measuring profitability, growth and balance-sheet strength from non-valuation factors.

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.