Network pilot
Case Study: Repair AI in a Leading Chinese New-Energy Commercial Vehicle After-Sales Network
A four-week pilot across 10 designated service stations recorded 99 real repair queries and technician-reported time savings.
Verdict First
A leading Chinese new-energy commercial vehicle manufacturer piloted Repair AI across 10 designated authorized service stations for four weeks, with 12 accounts activated.
In a follow-up survey of five deeply participating service stations, 100% of respondents reported efficiency gains, and 80% said a typical two-hour repair order could save at least 20 minutes. The median reported time saving was 20–30 minutes per order, while the highest response exceeded one hour on a single repair task.
These are technician-reported efficiency results from the pilot.
The Challenge: More Complex Vehicles, More Knowledge to Search
The customer’s after-sales network was supporting rapidly evolving new-energy commercial vehicles across multiple powertrain configurations.
The pilot identified several recurring frontline challenges:
- high-voltage and electric-drive systems increased diagnostic complexity;
- new vehicle platforms and service information were updated quickly;
- technician experience varied across service stations;
- repair manuals, wiring information and technical guidance could require repeated searching during a repair.
The purpose of the pilot was to test whether AI-assisted diagnosis could reduce the time technicians spent determining a troubleshooting direction and locating relevant repair information.
The Solution: Repair AI Embedded in the Existing Service Workflow
Repair AI was embedded into the customer’s existing after-sales service entry, so technicians could use AI assistance without switching to a separate standalone workflow.
During real repair work, technicians used Repair AI for:
- symptom-based diagnosis;
- DTC fault-code analysis;
- wiring-diagram and service-information retrieval;
- repair procedures and maintenance guidance;
- follow-up questions as new symptoms or measurements appeared.
The tool was positioned as an assistant: technicians used AI to narrow the search direction and then referred to the relevant repair information and source documents as needed.
Pilot Usage: 99 Real Repair Queries
During the four-week pilot:
| Metric | Result |
|---|---|
| Designated pilot service stations | 10 |
| Activated accounts | 12 |
| Total repair queries | 99 |
| Active days | 18 |
| Result-page visit rate | 94.9% (94/99) |
The query mix showed that technicians were using Repair AI across several real workshop scenarios:
| Query category | Share |
|---|---|
| Symptom diagnosis | 43.4% |
| New-energy high-voltage / charging | 23.2% |
| Repair procedure guidance | 18.2% |
| DTC diagnosis | 8.1% |
| Electrical / body | 5.1% |
| Chassis / braking / drivetrain | 2.0% |
Symptom diagnosis and new-energy high-voltage / charging together accounted for about two-thirds of all queries, making them the most prominent needs observed in the pilot.
Technician Feedback: Measurable Time Savings
Five deeply participating service stations completed the post-pilot survey.
| Survey metric | Result |
|---|---|
| Respondents confirming efficiency improvement | 100% |
| Respondents saying a typical 2-hour order saves ≥20 min | 80% |
| Median reported saving per order | 20–30 min |
| Highest reported saving | 1+ hour |
| Overall satisfaction | 80% |
| Willingness to recommend | 100% |
| Willingness to continue using | 100% |
| Respondents reporting daily use | 60% |
The strongest verified conclusion from the pilot is straightforward: technicians reported meaningful reductions in the time spent on repair work, especially when diagnosis required symptom reasoning or extensive information retrieval.
What the Pilot Proves
The pilot provides direct evidence of:
- real use across multiple authorized service stations;
- strong technician-reported efficiency gains;
- repeated use for symptom diagnosis and new-energy faults;
- high willingness to continue using and recommend the tool.
Why This Matters for After-Sales Networks
The pilot shows a practical role for AI in a service network: helping technicians narrow down fault directions and locate relevant information faster while keeping the technician in control of the repair decision.
For manufacturers, the same workflow can also create a structured view of what frontline technicians are repeatedly searching for — such as charging faults, high-voltage issues, sensor warnings and repair-procedure questions — providing useful signals for future content and product improvement.
FAQ
What is Repair AI?
Repair AI is an AI-assisted automotive repair tool for symptom diagnosis, fault-code analysis, repair-information retrieval and step-by-step troubleshooting support.
How much time did technicians report saving?
80% of surveyed respondents said a typical two-hour repair order could save at least 20 minutes. The median reported saving was 20–30 minutes, with the highest response exceeding one hour.
How large was the pilot?
The four-week pilot covered 10 designated authorized service stations, with 12 accounts activated.
Was Repair AI used in real repair work?
Yes. The pilot recorded 99 repair queries across 18 active days, covering symptom diagnosis, new-energy high-voltage and charging faults, repair guidance and DTC analysis.
Does Repair AI replace technicians?
No. Repair AI helps technicians narrow down diagnostic directions and retrieve relevant repair information; the technician remains responsible for verification and repair decisions.