type.inbound
and (
strings.icontains(body.current_thread.text, "ezra", "hesion")
or regex.icontains(body.current_thread.text, 'better\s?up', 'coach\s?hub')
)
and strings.icontains(strings.replace_confusables(body.current_thread.text),
'leadership development coach',
'coaching, leadership development',
'accounting & collections department',
'executive coach for emerging leaders',
'accounts receivable - coaching division'
)
and any(ml.nlu_classifier(body.current_thread.text).topics,
.name in (
"Financial Communications",
"Payment Information",
"Request to View Invoice"
)
and .confidence == "high"
)
and any(ml.nlu_classifier(body.current_thread.text).intents, .name == "bec")
Playground
Test against your own EMLs or sample data.