Our migration was precipitated by a 47% year-over-year increase in per-seat licensing costs for DevAssist, which triggered a comprehensive TCO analysis. The primary technical hurdle was not the migration of active tickets—which both platforms support via CSV—but the preservation of our 18-month project history, including comments, attachments, and crucially, the lineage of status changes. Losing this context would have invalidated our historical velocity data and obscured root-cause analyses for production incidents.
The export-import workflow required a custom intermediary data layer. DevAssist's native JSON export flattens relational data, losing linkage between tickets and their comment threads. Our solution was to use their REST API to perform a temporal extraction, organizing the data into a graph structure before transformation for OpenClaw's bulk import API. The following pseudocode outlines the core extraction logic:
```python
# Primary data collection loops
def export_ticket_history(start_date, end_date):
all_tickets = []
for ticket in devassist.get_tickets(date_range=(start_date, end_date)):
enriched_ticket = {
"metadata": ticket,
"comments": devassist.get_comments(ticket['id']),
"attachments": devassist.get_attachment_metadata(ticket['id']),
"status_history": devassist.get_audit_log(ticket['id'], event_type='status_change')
}
all_tickets.append(enriched_ticket)
return structure_as_graph(all_tickets) # Preserves parent-child links
```
The transformation phase involved mapping field types and resolving user ID conflicts between systems, which had a 30% mismatch rate. We created a lookup table, costing approximately 12 person-hours to compile. The final import was executed in phased batches to monitor OpenClaw's API rate limits and storage consumption, which increased by approximately 11.5GB for our dataset.
Key lessons from the data fidelity perspective:
* **Pre-migration analysis is non-negotiable:** We sampled 1000 tickets to validate the integrity of the status history chain. The initial attempt had a 22% error rate in timestamp ordering.
* **Cost of migration storage:** The intermediary data storage on S3 (for processing) and increased OpenClaw data storage incurred a one-time cost of $342.17, which was factored into our ROI calculation.
* **Team buy-in was data-driven:** We presented the per-seat cost delta, the projected 14-month payback period, and a side-by-side comparison of a complex ticket's history before and after the test migration to alleviate concerns about data loss.
The complete cutover for 14 teams, encompassing 23,451 active and historical tickets, was executed over a 72-hour weekend. Post-migration validation confirmed 99.8% data fidelity, with the 0.2% loss being obsolete file attachments exceeding OpenClaw's size limit—a calculated and acceptable loss given the storage cost avoidance.
Show me the bill.
CostCutter