DemoDemo Environment — all customers, addresses, jobs, financials and communications shown are fictional.
Boerne 96°F · 20% rain (demo)
Feedback
Demonstration data. Inbound messages, AI classifications and routing decisions are simulated. No live mailbox, GoHighLevel account or parsing service is connected.
Inbound analytics
Classification quality, automation rate and the dollars flowing through the inbound rail.
Rail performance
Auto-route rate
74%
Avg handle time
3.5m
Receipt to routed
Human touches
9
Leads from email
11
Documents filed
14
Dollars attached
$91,781
Auto-route rate — last 14 days
Classification accuracy by intent
Rolling 30 days (demo)
Intent breakdown
| Intent | Messages | Avg confidence | Dollars attached |
|---|---|---|---|
| New Lead | 11 | 88% | — |
| Supplement Response | 3 | 95% | $19,795 |
| Supplier Invoice | 3 | 88% | $45,766 |
| Insurance Document | 3 | 90% | $20,650 |
| Permit Or Inspection | 2 | 94% | — |
| Warranty Or Service | 2 | 89% | — |
| Carrier Correspondence | 2 | 91% | — |
| Change Order Response | 2 | 90% | $5,570 |
| Homeowner Question | 2 | 87% | — |
| Spam | 2 | 95% | — |
| Unknown | 2 | 37% | — |
| Subcontractor Document | 1 | 94% | — |
Corrections made in the review queue are the training signal: every reclassification updates the mapping rules for that sender pattern, so accuracy climbs as the team triages.