A DTC SaaS deflected 58% of support tickets before a human saw them.
Industry: DTC SaaS · Geography: US · Scale: ~25 staff
Before
Before
DTC SaaS
After
58%
of tickets resolved without a human
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Support volume scaled faster than headcount. Same five questions landed in the inbox 200 times a week; agents answered them on autopilot and burnout was rising.
What staying manual actually costs.
- 01Time spent on the same manual work every weekBeforeDTC SaaS
- 02Decisions bottlenecked on one person being available60%+When they were heads-down, on PTO, or sick, the work stalled.
- 03Forecast confidence before the system shippedLowBecause the data was always a week behind reality.
An AI-assisted inbox that classifies every incoming ticket, answers the ones it can from the help center and order data, and routes the rest to the right agent with full context attached.
The system, end to end — press play to see it run.
- 01Classify
Every incoming message is tagged by topic, urgency, and whether it's resolvable from existing knowledge.
pending - 02Draft or resolve
Routine questions get an instant, grounded answer pulled from the help center. The rest get a draft reply for the agent.
pending - 03Route with context
Escalated tickets land in the right queue with the customer's history, recent orders, and the AI's draft.
pending
58%
of tickets resolved without a human
~3 min
average resolution time, down from ~4 hrs
↑ CSAT
scores, not down, after launch
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Response time
≤ 4 business hours
Coverage
USA · UK · EU
Team
10 engineers · 1 PM