This research investigates how invisible data markers can be embedded into woven textiles and retrieved using near-infrared (NIR) imaging. It focuses on integrating machine-readable information without altering the textile’s visible appearance, which is essential for traceability and compliance with emerging frameworks like the Digital Product Passport (DPP). The method follows a multi-stage approach, beginning with an analysis of material behavior and the combination of yarns with differing NIR absorption properties. Transparency and contrast serve as key evaluation criteria. Results demonstrate that specific yarn combinations in compound woven structures can be programmed with invisible markers that are detectable under NIR light yet remain invisible to the human eye. A final demonstrator validates the approach. This supports circular design by embedding product data within the material itself and contributes to human–computer interaction (HCI) by enabling non-electronic, material-based interfaces. The work advances research on embedded markers and tags, enabling machine-readable codes without additional hardware.