Fusemachines Inc (FUSE) Fair Value & Analysis
Technology · US · Market cap $33.3M
Fair value as of: Jun 23, 2026
Analysis
Fusemachines Inc (FUSE) currently trades at $1.01, while our model-based Fair Value estimate is $0.6700 — implying the stock looks roughly 33.3% overvalued today. We read business quality at 91/100 (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
Fusemachines Inc. develops and delivers artificial intelligence (AI) as a service and machine learning software solutions in North America, Latin America, and Asia. The company offers AI studio, a platform that offers to build, deploy, and manage AI applications; AI Engines, a software solution to extract information from complex documents, generate accurate answers and summaries from large datasets, and detect suspicious activities and patterns in financial transactions at retail outlets; and AI Agents, that automates workflow, such as interview assistant, assisting recruiters by asking role-specific questions, and evaluating responses. Further, the company offers managed outbound services and AI as a service for data management, machine learning, and analytics. Additionally, it offers Fusemachines AI fellowship program, which provides selected scholars from underserved communities with mentorship and resources to develop advanced skills in artificial intelligence and machine learn…
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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.