SoundHound AI, Inc (SOUN) Fair Value & Analysis
Technology · US · Market cap $3.5B
Analysis
SoundHound AI, Inc (SOUN) currently trades at $6.44, while our model-based Fair Value estimate is $3.48 — implying the stock looks roughly 46.0% 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: low).
About the company
SoundHound AI, Inc. provides independent voice artificial intelligence (AI) solutions that enables businesses across the automotive, TV, IoT, and customer service industries to deliver conversational experiences to customers in the United States, Korea, France, Japan, Germany, and internationally. The company offers Houndify platform, which provides a suite of Houndify tools to help brands build conversational voice assistants, such as Application Programming Interfaces (API) for text and voice queries, support for custom commands, extensive library of content domains, inclusive software development kit platforms, collaboration capabilities, diagnostic tools, and built-in analytics; SoundHound Chat AI, a voice assistant with integrated generative AI that integrates with knowledge domains, pulling real-time data, such as weather, sports, stocks, flight status, restaurants, and other data; and SoundHound Smart Answering, which offers customer establishments the option to build an easy…
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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.