BlueSword Intelligent Technology Co (688557) Fair Value & Analysis
Industrials · CN · Market cap 2.9B CNY
Fair value as of: Jun 24, 2026
Analysis
BlueSword Intelligent Technology Co (688557) currently trades at ¥31.40, while our model-based Fair Value estimate is ¥21.67 — implying the stock looks roughly 31.0% overvalued today. We read business quality at 95/100 (high quality), in the Industrials 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: high).
About the company
BlueSword Intelligent Technology Co., Ltd. operates as a logistics system solution provider in China. The company provides consulting and planning, software engineering, equipment manufacturing, system integration, and Robot as a Service solutions, as well as cloud computing, and quality control services. It also offers logistics robots, including stacker cranes, multi-shuttles, AGVs and AMRs, aerial shuttles, picking and sorting robots, packaging machines, depalletizer and palletizer robots, loading and unloading robots, high-speed lifts, and convey sorters, as well as smart solutions. In addition, the company provides artificial intelligence, such as SLAM navigation, AGV monitoring and simulation integration, petri net model depalletizing and palletizing algorithm, pattern recognition algorithm, high-order S-curve motion control, intelligent shape detection algorithm, micro-servo claw control, dynamic and flexible storage spot algorithm, intelligent inbound dispatching, order dist…
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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.