Aurrigo International plc (AURR) Fair Value & Analysis
Consumer Cyclical · GB · Market cap 64.8M GBX
Fair value as of: Jun 24, 2026
Analysis
Aurrigo International plc (AURR) currently trades at p0.6850, while our model-based Fair Value estimate is p0.4400 — implying the stock looks roughly 35.8% overvalued today. We read business quality at 92/100 (high quality), in the Consumer Cyclical 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
Aurrigo International plc offers electrical components to the automotive and aviation industry in the United Kingdom, Europe, and internationally. It operates through two segments, Automotive components and Autonomous. The Automotive components segment offers components and systems, including electronic control units, wiring harness systems, interior and exterior parts, and safety-critical systems, as well as trim and design components. The Autonomous segment designs, develops, and manufactures autonomous vehicles; and provides associated autonomous design and consultancy services. In addition, the company's hardware solutions include Auto-DollyTug, an autonomous baggage and cargo handling vehicle; Auto-Cargo, an autonomous vehicle for the handling of heavier cargo loads; and Auto-Shuttle, an electric and autonomous ten-seat passenger vehicle to provide airside transport for crew, engineers, and staff. Further, it offers software solutions, including autonomous driving software stac…
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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.