Robotics
Industrial robots precisely adapted for garment handling, seam routing, and positioning.
Technology
An integrated automation approach for flexible textiles
Vision at every stitch — closed-loop control at sewing speed.
The challenge is not just moving fabric, but creating a stable, monitored and scalable process around an unstable material.
Core technology building blocks
Textile Handling
Proprietary system managing limp, deformable fabric with industrial repeatability.
AI Trajectory Planning
AI continuously optimises robot motion per garment type — adapting in real time.
Process Control
Digital sequencing ensuring consistent quality, throughput, and production targets.
Operations & Monitoring
Live KPIs, anomaly detection, and remote diagnostics for industrial operators.
Process workflow
Material feed
Supply of textile semi-finished goods.
Pickup and positioning
Controlled gripping and alignment of flexible material.
Guided process execution
Textile support during sewing, welding, bonding or handling operations.
Monitoring and feedback
Sensor input supports process stability and traceability.
Data and production logic
Interfaces and software provide visibility beyond one isolated motion.
AI-driven textile automation
Vision and Learning for Automation
ADOTC uses image analysis and reinforcement learning to improve robotic perception, decision-making, and process stability in textile automation.
This helps robots better understand and respond to the behaviour of flexible materials, enabling more reliable handling, guidance, and joining of textiles under changing process conditions.
Understanding Flexible Material Behaviour
The goal is to enable robots to better interpret and respond to the behaviour of flexible materials.
This is essential because textiles can deform, shift, stretch, or fold during handling and sewing, welding or bonding, making automated processing significantly more complex.
Continuous Learning in Real Production
Our robots adapt to process variation and continuously improve execution quality in real production environments.
By learning from process data and visual feedback, they can respond more robustly to changing material behaviour, tolerances, and operating conditions.
This supports more stable processes, higher repeatability, and more reliable automation in everyday textile production.
Technology Platform
ADOTC combines its building blocks — robotic handling, process-specific end effectors, machine vision, AI-based control and a connected software layer — into one configurable platform. Rather than a fixed single-purpose machine, the same platform is adapted to different seam paths, products and materials, which is what makes flexible, scalable textile automation possible.
Partners & funding
Next step
Let’s evaluate your textile process
Tell us about your product, production step or automation challenge. We will assess whether a robotic approach is technically meaningful for your application.