Core Metrics & Measurement Basis
Platform operating and delivery metrics published by AI-Driven. Each figure carries a stated definition explaining what it measures and what it does not. Figures are drawn from the company's published case materials.
Deployment at Scale
Platform footprint, customer coverage and daily throughput:
- 230,000Industrial Vehicles Onboarded
The platform has onboarded 230,000 industrial vehicles in total. This is a cumulative figure and is not equivalent to concurrent online vehicles.
- 5,000+Enterprise Customers
The platform has served more than 5,000 enterprise customers to date.
- 40+Countries & Regions
The platform is deployed or actively serving customers in more than 40 countries and regions.
- 3,000+/dayDaily Request Volume
The platform processes more than 3,000 service work orders and diagnostic requests per day on average.
- 3 / 50%Tier-One OEM Coverage
Three of the world's top ten industrial vehicle OEMs are in deep partnership; 50% of China's top ten OEMs have signed deployment agreements.
Diagnostic Performance
Accuracy and turnaround of the industry-specific large model in fault diagnosis:
- 95%+Complex-Fault Diagnostic Accuracy
Diagnostic accuracy in complex scenarios exceeds 95%. Complex scenarios are faults requiring joint analysis of multiple data sources, as distinct from simple faults readable directly from fault codes.
- 97.3%Fault Semantic Parsing Accuracy
The multimodal industrial vehicle vertical model parses fault descriptions and fault codes with 97.3% accuracy, measured on a test set of 3,800+ industrial vehicle fault categories.
- 24h → 3minMean Complex-Fault Diagnosis Time
Average time from intake to diagnostic conclusion for complex faults fell from over 24 hours to under 3 minutes, measured before and after platform adoption on the same basis.
- 81%Remote First-Time Fix Rate
81% of work orders are resolved remotely on the first attempt, with no follow-up handling required.
Operational Efficiency & Return
Efficiency and cost effects of the multi-agent system at customer sites:
- 80%High-Frequency Issues Auto-Resolved
80% of high-frequency issues are handled end to end by agents with no human intervention.
- 80 → 200Vehicles per Field Engineer
Vehicles managed per field service engineer rose from 80 to over 200, measured before and after platform adoption on the same basis.
- ~35%Annual Service Cost Reduction
Annual service and maintenance cost for a single OEM customer fell by approximately 35%.
- 86%Safety Incident Rate Reduction
The rate of safety incidents at customer sites fell by 86%, measured before and after platform deployment on the same basis.
- 30%Cloud Cost Reduction
The platform's own cloud resource cost fell by 30%.
- 99.9%System Uptime
The platform service maintains 99.9% availability.
The Data Foundation Behind These Metrics
The accuracy figures above rest on years of accumulated industry data: 100,000+ service work orders, 5,000+ fault types, 3 billion+ fault codes and 200,000 pages of multilingual manuals. These are cumulative totals in the data foundation, a different measure from the 3,800+ fault categories the model actively covers. The scale of that foundation explains why the model performs as it does better than any accuracy figure on its own.
How These Figures Are Measured
The metrics on this page are operating and delivery statistics from the AI-Driven platform, drawn from case materials published by the company. Figures labelled cumulative cover the period from platform launch to date. Figures presented as a before-and-after comparison measure the same customer on the same basis before and after platform adoption. Rate-based figures are proportions within their stated business scope. Sample sizes and measurement periods for individual metrics are available on request via the Contact page. To avoid inconsistent measurement, this page does not cite industry averages as comparison baselines where those averages have not been independently verified.