Palladyne AI Corp (PDYN) Fair Value & Analysis
Technology · US · Market cap $331M
Fair value as of: Jun 24, 2026
Analysis
Palladyne AI Corp (PDYN) currently trades at $6.47, while our model-based Fair Value estimate is $3.22 — implying the stock looks roughly 50.2% overvalued today. We read business quality at 95/100 (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
Palladyne AI Corp., a technology company, develops and offers embodied artificial intelligence software and collaborative autonomy solutions in the United States. Its Artificial Intelligence (AI)/ Machine Learning (ML) Foundational Technology enables robots to observe, learn, reason, and act in unstructured. In addition, the company's technologies enable robotic systems to perceive their environment and quickly adapt to changing circumstances by generalizing from their experience using dynamic real-time operations without extensive programming, training, or the latency associated with processing in the cloud. Further, it offers Palladyne IQ used with industrial robots and cobots, enabling them to learn multiple tasks and handle disruptions or obstacles; and develops Palladyne Pilot for use with unmanned platform, such as UAVs to enable persistent detection, identification, tracking, and classification of objects of interest by sharing situational awareness information across multipl…
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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.