AEye, Inc (LIDR) Fair Value & Analysis
Technology · US · Market cap $67.2M
Fair value as of: Jun 25, 2026
Analysis
AEye, Inc (LIDR) currently trades at $1.33, while our model-based Fair Value estimate is $1.73 — implying the stock looks roughly 30.1% undervalued today. We read business quality at 95/100 (high quality), in the Technology sector. Bull case: trading below our estimate, it may offer upside if the fundamentals hold. Bear case: a low price can be a value trap when quality is weak or the data is thin (evidence: low) — always confirm before acting.
About the company
AEye, Inc., together with its subsidiaries, provides physical AI sensing solutions for vehicle autonomy, advanced driver-assistance systems (ADAS), robotic vision applications, and non-automotive applications in the United States, Europe, and the Asia Pacific. The company offers Apollo, an intelligent sensing lidar platform for ADAS and autonomous vehicles applications, as well as non-automotive market, including rail, construction, mining and agriculture, aerospace and defense, security and foreign object detection, and intelligent transportation systems. It also provides STRATOS for applications requiring enhanced long-distance performance comprising certain automotive, infrastructure, aviation, industrial, and defense sensing environments; and OPTIS, a full-stack physical AI solution for the non-automotive market. It sells its products through direct sales in the automotive and non-automotive markets; and system integrator channel partners for the non-automotive market. AEye, Inc…
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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.