Ecommerce support triage
Inbound messages are classified, enriched with order context and routed by risk.
Case note
The implementation was treated as a small operating system: visibility first, ownership next, automation only after the workflow was clear.
The starting point
Order questions, refund requests, product issues and complaints were landing in the same queue. Agents spent the start of each shift sorting messages before they could actually help customers.
The important signal was not that the team was working badly. The issue was that work depended on memory, copied data, parallel system checks and priority decisions without a shared source of truth. The first value was turning invisible work into a visible workflow.
The implementation
Ductio added a triage layer that classifies each ticket, looks up order context, drafts safe responses for common questions and sends sensitive cases to a human escalation lane.
The scope stayed deliberately small. Rules cover repeatable work, AI summarizes or classifies where free text adds context, and sensitive decisions remain in human review. AI as support inside the process, not as autopilot.
What was used
Tooling was chosen from the process outward, not from a pre-decided technical preference. Each piece needed a clear owner, a stable integration path and a simple way to inspect errors.
In practice, the build combined Helpdesk, Order API, AI classifier, Knowledge base, Slack, n8n. The tools visible to the team stayed close to their daily work, while integration logic was documented and kept separate from sensitive commercial decisions.
The improvement showed up in daily work.
Rather than treating the result as a dashboard, the team felt it in three specific moments: less manual preparation, less context hunting, and fewer doubts about who needed to act.
Triage time: Average first sorting effort moved from 18 min to 7 min.
Queue clarity: Tagged tickets with clear next action moved from Low to High.
Escalation signal: Sensitive cases surfaced early moved from Manual to Flagged.
What changed after launch
Support agents start with context and a suggested path. The system does not hide complex cases; it makes them easier to spot.
The most valuable change was operational calm. The team stopped chasing fragments and started working from a shared sequence: intake, context, decision, action and evidence. 60% faster triage
The workflow in one line
How it was built
The workflow listens to new helpdesk tickets, applies a classifier, retrieves order metadata, writes a response draft when confidence is high and updates the ticket with category, risk and next action.
The stack was pragmatic: Helpdesk, Order API, AI classifier, Knowledge base, Slack, n8n. Tools were chosen for ownership, integration and maintainability, not for theater. The result is a system the team can understand and operate.
What was delivered
- Ticket classification
- Order lookup
- FAQ draft
- Escalation routing
- Agents started with category, context and suggested next action.
- Simple replies were drafted for review.
- Risk labels made urgent complaints easier to spot.
