Jira Integration
Trigger vehicle recommendations from Jira issues, assign vehicles in Fleetwise, and sync status updates back to Jira.
Fleetwise supports full test planning and vehicle management natively — for most teams, working directly in Fleetwise is the simplest and most complete approach. However, some organisations manage test requirements in Jira and want to keep that workflow while using Fleetwise for the physical fleet. The Jira integration connects the two so that new test requirements trigger vehicle recommendations in Fleetwise, and allocation status flows back to Jira without manual updates.
Setup Required
The Jira integration is configured per workspace with support from the Wise Applications team. Contact us to discuss your Jira workflow and set up the connection.
How It Works
The flow step by step
A test issue is created in Jira
A team member creates a new issue in Jira representing a test requirement — for example, a durability test that needs a specific vehicle type for two weeks starting in April.
Fleetwise receives the trigger
A webhook or API call sends the issue data to Fleetwise. The AI reasoning layer interprets the Jira fields (summary, description, custom fields for dates, vehicle type, specs) and maps them to Fleetwise request fields — spec codes, attributes, usage window, and required duration.
Fleetwise recommends vehicles
Based on the interpreted requirements, Fleetwise searches for matching vehicles by spec codes, attributes, and availability. It returns a ranked list of recommended vehicles, scored by how well they match the requirements.
A vehicle is assigned
The Fleet Coordinator reviews the recommendation and confirms the allocation in Fleetwise — or the integration auto-assigns the top-ranked vehicle if configured to do so.
Status syncs back to Jira
As the allocation progresses in Fleetwise (confirmed, in use, completed), status updates are posted back to the Jira issue. The assigned vehicle, allocation dates, and current status appear directly on the issue so the test planning team stays informed without leaving Jira.
What Gets Synced
| Direction | Data | Example |
|---|---|---|
| Jira to Fleetwise | Test requirements, dates, vehicle type, priority | "Durability endurance test, BEV powertrain, 2 weeks from April 7" |
| Fleetwise to Jira | Recommended vehicle, allocation status, assigned dates | "Vehicle Proto-F assigned, allocation confirmed, April 7–21" |
| Ongoing updates | Status changes, completion, mileage logged | "Allocation complete, 3,200 KM logged" |
The AI Reasoning Layer
Test requirements in Jira are often written in natural language or use custom field conventions that differ from Fleetwise's structured format. The AI reasoning layer bridges this gap:
- Interprets unstructured text — extracts vehicle type, powertrain, test duration, and date requirements from issue summaries and descriptions.
- Maps custom fields — translates Jira custom fields (e.g. "Vehicle Category", "Test Start Date") to Fleetwise spec codes, attributes, and usage windows.
- Handles ambiguity — when requirements are vague, the AI layer makes reasonable inferences and flags anything it could not confidently map for human review.
AI Layer Is Optional
If your Jira workflow already uses structured fields that map directly to Fleetwise request fields, you can bypass the AI layer and use direct field mapping instead.
Example Scenario
A durability team manages their test plan in Jira. Each test is a Jira issue with custom fields for vehicle type, powertrain, test location, and target dates.
| Jira Field | Value | Fleetwise Mapping |
|---|---|---|
| Summary | "Endurance test - urban cycle" | Request name |
| Vehicle Type | "SUV" | Spec code family |
| Powertrain | "BEV 75kWh" | Spec code |
| Test Location | "HQ" | Work package context |
| Start Date | "2026-04-07" | Usage window start |
| Duration | "14 days" | Required duration |
| Priority | "High" | Request priority |
When this issue is created, Fleetwise receives the data, matches it against available vehicles at HQ with the BEV 75kWh spec, and returns a recommendation. The coordinator confirms the allocation, and the Jira issue is updated with the assigned vehicle and dates.
As the test runs, mileage logs and status changes in Fleetwise are reflected on the Jira issue, giving the planning team full visibility without switching tools.
Best Practices
- Use consistent Jira fields. The more structured your Jira issues, the more accurate the mapping to Fleetwise. Custom fields for vehicle type, dates, and location produce better results than relying on free-text parsing alone.
- Review recommendations before auto-assigning. Start with manual confirmation of vehicle recommendations to validate the mapping quality. Once confident, you can enable auto-assignment for routine requests.
- Keep status sync bidirectional. When a Jira issue is cancelled or dates change, the integration should update or cancel the corresponding Fleetwise request to avoid stale allocations.
- Link to the right work package. If your Jira project maps to a specific Fleetwise work package (e.g. by location or department), configure the integration to route requests to the correct work package automatically.
Related
- Vehicle Requests — the request workflow that Jira issues feed into
- Organizing Your Fleet — structuring work packages so integrations route to the right scope
- Key Concepts — how requests, allocations, and work packages connect