Industrial AI
Engineered for Impact
Start with the AI training under the EU AI Act or the one-day potential workshop — both at a fixed price. Implementation afterwards: knowledge management, computer vision, predictive maintenance, and process optimization.
Getting started
Fixed price, short buying cycle – this is where most companies start.Implementation
Project work after the workshop or pilot.Knowledge Management & RAG
Stop wasting hours searching for technical documentation, machine manuals, or maintenance logs. I build private Retrieval-Augmented Generation (RAG) systems that let your employees chat securely with all of your company data.
- Chat directly with PDF manuals, CAD specs, and Excel logs
- 100% data privacy: On-Premise or secure Private Cloud
- Microsoft SSO, AD group sync, and document-level security
- Reduce search and onboarding time by up to 80%
TyrolAI Docs — my own enterprise RAG platform based on IBM's OpenRAG, hardened for Austrian industry. Proof that I don't just consult, I build. Available in project work on request.
Enterprise Knowledge Chat
Predictive Maintenance
Stop performing maintenance too early (wasting parts) or too late (machine breakdown). By analyzing vibration, acoustic, and temperature data through Edge AI, I predict component failures weeks in advance.
- Vibration analysis on CNC spindles to detect bearing wear
- Acoustic anomaly detection in complex gearboxes
- Up to 40% reduction in unplanned downtime
Anomaly Detection Score
Predicted Failure in
14 Days
Process Optimization & RL
Machine parameters are often set by gut feeling. I use Deep Reinforcement Learning and Digital Twins to permanently calculate the mathematical "Golden Batch" for your PLCs.
- Dynamic adjustment of cutting speeds based on material variance
- Reducing cycle times by 10-15% without sacrificing quality
- Energy consumption minimization via intelligent scheduling
Computer Vision & Quality Control
Say goodbye to human error in defect detection. I deploy state-of-the-art neural networks (CNNs, Vision Transformers) combined with industrial high-speed cameras to inspect products at speeds and accuracies no human can match.
- Detection of micro-scratches on metal surfaces (down to 0.1mm)
- Dimensional accuracy checks without manual measuring tools
- Classification of organic or unpredictable material defects
def inspect_part(image_tensor):
# Run optimized ResNet50
prediction = model(image_tensor)
defect_score = prediction['defect_prob']
if defect_score > 0.95:
trigger_pneumatic_ejector()
log_anomaly(defect_score)
return "REJECT"
return "PASS"