Generative AI for Transport
Monitor rail systems from any OEM in one AI platform
Towards data-driven decision making using Digital Twins and Generative AI for whole fleet monitoring.
Return on Investment
To first insight
Data-driven decision support system for maintenance and fleet teams in rail.
Amygda's unique approach enables operators to monitor multiple system data beyond just fault alarms and provides a holistic view of the whole fleet irrespective of any OEM on a single platform.
Helping TOCs, ROSCOs and other asset owners reduce the total cost of ownership with predictive breakdown management to lower maintenance costs and increase uptime.
Benefits for rail TOCs, ROSCOs,
and other asset owners
Identify recurring issues and their root causes
Optimise maintenance by identifying fleet-wide trends and patterns through data analysis.
AI to predict issues based on historical ops
Use data from previous maintenance events to identify potential failure patterns and monitor future events.
Holistic view of your whole fleet centralised
Amygda's unique approach enables operators to monitor multiple system data beyond just fault alarms and provides a holistic view of the whole fleet irrespective of the OEM, on a single platform.
Amygda helps transport businesses utilise existing data to unlock actionable insights.
Generative AI Maintenance
Save costs and time by utilising historical knowledge to uncover fastest path to resolution. Acts as Engineer-assistant.
Zero Unplanned Downtime
Providing advance alerts for maintenance and optimising to avoid downtime, increase asset utilisation, and reduce repair costs.
Whole fleet monitoring
All of your connected fleets data in one platform
Improving fleet uptime
with better root cause analysis and work scope
Digital Twin of any equipment
for equipment or fleet irrespective of any OEM
Connects to existing data sources
and APIs to ingest data from any ERP system
Real time decision and alerting
using data analytics on RCM or event-data
High-frequency streaming data
Utilising the world's fastest time-series DB for analysis
Case Study: Rail Vehicle Health Monitoring
A large UK rail company reduces train breakdowns by 52% and saves £520,000, improving customer satisfaction and minimising delay penalties.
Read Case Study
Arrange a Demo
Find out how you can optimise your maintenance operations and save money with OEM-neutral fleet monitoring
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