Industries
AI for Manufacturing
AIYatch builds AI for manufacturers that reduces downtime, defects and planning effort: visual quality inspection, predictive maintenance from sensor data, production and demand planning, and document automation for orders and supplier paperwork. Models are grounded in your ERP, MES and machine data and can run at the edge on the factory floor when connectivity or latency demands it.
Typical challenges
- Manual inspection misses defects and does not scale with line speed.
- Unplanned machine downtime is expensive and maintenance is still schedule-based.
- Production planning relies on spreadsheets and tribal knowledge.
- Order, delivery and supplier documents are keyed in by hand into ERP systems.
Typical use cases
Visual quality inspection
Camera-based defect detection and segmentation models trained on your parts, running on edge devices and integrated with line control and reporting.
Predictive maintenance
Anomaly detection and remaining-useful-life models over vibration, temperature and PLC data that trigger maintenance before failure.
Production and demand planning
Forecasting and optimisation models that align production schedules with demand, capacity and material availability.
Supplier and order document automation
Document AI that extracts purchase orders, delivery notes and certificates and posts them into ERP with exception handling.
Shop-floor knowledge assistant
A RAG assistant over manuals, SOPs and maintenance logs that answers technician questions on a tablet, with citations to the source page.
Questions from this sector
Yes. We train models in the cloud or on your servers and deploy optimised versions to edge devices or industrial PCs on the line. Inference happens locally with millisecond latency; results and sample images sync to a central dashboard when a connection is available.
Often a few hundred labelled images per defect type is enough to start, especially with pre-trained models and augmentation. Rare defects are handled with anomaly-detection approaches that learn what “good” looks like. We confirm feasibility on a small sample before committing to full delivery.
Through the systems’ APIs, database views or file exports, depending on what is available. We build a thin integration layer with logging and retries so AI outputs appear as work orders, quality records or planning inputs inside the tools your teams already use.
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