SeeWay.ai Co (002405) Fair Value & Analysis
Technology · CN · Market cap 17.4B CNY
Fair value as of: Jun 25, 2026
Analysis
SeeWay.ai Co (002405) currently trades at ¥7.17, while our model-based Fair Value estimate is ¥0.9600 — implying the stock looks roughly 86.6% overvalued today. We read business quality at 94/100 (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
SeeWay.ai Co., Ltd., together with its subsidiaries, provides full-stack automotive intelligence solutions in China. It offers software-hardware integrated solutions for multiple levels and complex driving scenarios; intelligent cockpit software product, such as smart assistant, LBS, driving safety, audio and video entertainment, smartphone-vehicle connectivity, and ecological service; Intelligent cockpit hardware terminal product, including IVI, intelligent navigation, AVM panorama, cloud recorder, stream media, and in-vehicle HUD series; and user system, user mall and entertainment value-added services. The company also provides intelligent cockpit for commercial vehicles comprising intelligent connected devices, connected vehicle service platform, map service for commercial vehicle, and APP operation services; intelligent charging, smart mobility, and operation marketing services; and navigation for shared autonomy, data compliance and closed-loop, dynamic traffic, and high-preci…
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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.