C3.ai
C3.ai, listed in New York, sells enterprise AI software, including predictive maintenance that forecasts equipment failures from fleet data. Its main aviation customer is the US Air Force.
Founded in 2009 in Redwood City, California, with 1,001 to 5,000 staff, it sells C3 AI Readiness for military fleets and C3 AI Reliability for industrial assets. The Air Force's Rapid Sustainment Office uses C3 AI Readiness, also known as PANDA, as its system of record for predictive maintenance. No commercial airline customer is recorded.
No verified deployments yet
C3.ai is profiled in the companies register, but no deployment has been verified against published evidence. Records are added as evidence is reviewed and graded.
Products & Solutions
2 productsC3 AI Readiness
AI-powered predictive maintenance application for fleet operators that unifies sensor data, maintenance records, and parts inventory to identify when and how a component is likely to fail and recommends mitigation actions. Deployed for the U.S.
Military fleet operators face high aircraft downtime and costly unscheduled maintenance due to reactive maintenance approaches. C3 AI Readiness enables proactive, AI-driven maintenance scheduling and parts supply optimisation to increase mission availability and reduce lifecycle costs.
- Subsystem and component failure prediction using sensor-based algorithms (SBAs) and advanced ML (autoencoders, transformers, hierarchical models)
- Maintenance schedule and part lifecycle optimisation using survival analysis
- Generative AI-powered enterprise search for maintenance records and technical manuals
- Part inventory level optimisation and dynamic demand forecasting
4 more features
- Virtual Sensor Toolkit for rapid SBA model development
- Case management and alert workflow for maintenance coordinators
- Deployable to cloud, on-premise, or air-gapped environments
- Supports multiple aircraft platforms from a single extensible data model
Technical specifications (10)
- Predict failure risks with precision up to 99%
- Reduce parts replacements and supply by up to 30%
- Improve mission capability by up to 25%
- 3,110+ USAF aircraft monitored across 16 platforms
- 440,000+ analytics developed for USAF deployment
- 5,000 B-1B sorties ingested (75 billion rows of data) for model training
- Hosted on Air Force Cloud One (AWS GovCloud)
- Deployment from project kickoff to production in 12-24 weeks
- 92% alert accuracy on B-1B platform
- Up to 85% reduction in time from aircraft data extract to alert analysis
- Authority to Operate (ATO) at Impact Level 5 (IL5)
- Authority to Operate (ATO) at Impact Level 6 (IL6)
- Authority to Operate (ATO) at JWICS
- Authority to Operate (ATO) at SAP environments
- FedRAMP Moderate Authorization
C3 AI Reliability
AI-enabled predictive maintenance application that unifies sensor data, maintenance records, parts inventory, and process data to predict when and how an asset is likely to fail, enabling predictive monitoring at scale. Positioned for industrial asset operators across oil and gas, manufacturing, building operations, and energy.
Asset operators in heavy industry face costly unplanned downtime and inefficient maintenance scheduling. C3 AI Reliability enables early detection of equipment failure risk, reducing downtime by up to 50%, improving overall equipment effectiveness (OEE) by up to 5%, and eliminating alert noise by up to 99%.
- Unification of sensor data, maintenance records, parts inventory, and process diagrams into a flexible data model
- Advanced and flexible ML models for early, accurate equipment failure prediction
- Autogenerated failure mode identification and root cause analysis recommendations
- Generative AI-powered embedded search and chat interface for operational expertise
6 more features
- Case and work order management
- Asset onboarding and configuration
- Diagram parsing for process documentation
- End-to-end data and model configuration via application UI
- Scales to thousands of assets and millions of ML models
- Deploy in days; scale across sites in less than 6 months
Technical specifications (5)
- Reduce downtime by up to 50%
- Improve OEE by up to 5%
- Reduce alert noise by up to 99%
- Scale to thousands of assets and millions of models
- Ingests structured and unstructured data including sensor streams, maintenance records, and engineering documents
Profile data from the Aerostack companies register; deployment records from the verified register. Evidence grades: T1 first-party confirmation, T2 credible reporting. See Our approach for how records are graded.