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WeSort.AI builds hardware-integrated AI systems for automated material detection and sorting in recycling plants. The stack spans deep learning (TensorFlow, PyTorch), industrial control (Beckhoff, TwinCAT), and cloud infrastructure (Azure, GCP)—a hardware-software hybrid shape that reflects their core product: sensor modules paired with AI models. Active projects around ML training pipelines and image data collection from waste streams, combined with early-stage pain points (prototype-to-series transition, building sales from scratch, scaling funding processes), signal a company moving from R&D into commercialization.
Tech Stack 18 technologies
Core StackPython TensorFlow PyTorch Rust C++ TypeScript Angular Tailwind CSS Azure Autodesk Inventor Beckhoff Microsoft Office Excel PowerPoint Notion EPLAN TwinCAT GCP
What WeSort.Ai GmbH Is Building
◆Challenges
- Scalable data processing
- Ml training pipeline development
- Scaling product to market
- Building sales from scratch
- Scaling funding processes
- Identifying national funding programs
- Efficient sustainable growth
- Budget control and reporting
- Prototype to series transition
- On-site commissioning of new plants
▲Active Projects
- Ki-based sorting and recycling plant development
- Scalable funding process development
- Python data processing applications
- Ki-basierte sortieranlagen
- Prototypen inbetriebnahme
- Image data collection from waste streams
- Training ai models on waste data
- Creating demo models for customer presentations
- Ml training pipelines
- Funding program identification
Hiring Activity
Steady15 roles · 6 in 30d
Department
Seniority
Notable leadership hires: Chief of Staff
Company intelligence
