Healwell AI Inc (HWAIF) Fair Value & Analysis
Healthcare · US · Market cap $199M
Fair value as of: Jun 25, 2026
Analysis
Healwell AI Inc (HWAIF) currently trades at $0.5600, while our model-based Fair Value estimate is $0.1900 — implying the stock looks roughly 66.1% overvalued today. We read business quality at 95/100 (high quality), in the Healthcare 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
Healwell AI Inc. operates as a healthcare technology company in Canada, Australia, and New Zealand. The company operates in two segments, AI and Data Sciences, and Healthcare Software. It focuses on developing and commercializing clinical decision support systems to help healthcare providers detect rare and chronic diseases. The company provides healthcare artificial intelligence engines, electronic health record platform, healthcare data management and interoperability software, and early/late phase contract clinical research services. It also offers rare and chronic disease screening, real world evidence clinical study generation, advanced clinical decision support, and physical ASI co-pilot tools. It serves public sector organizations, healthcare providers, insurers, and life science companies. The company was formerly known as MCI Onehealth Technologies Inc. and changed its name to Healwell AI Inc. in September 2023. Healwell AI Inc. was incorporated in 2012 and is headquartered…
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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.