Omnichannel
AI Communication Monitoring System
A workflow that watches shared mailboxes and channels, classifies significant signals, creates tasks and escalates by role and SLA.
Context
Shared mailboxes and channels receiving operational signals that require timely response.
Problem
Operators manually scanned mail and channels, triaged incidents and tracked deadlines; response time grew and some signals were lost.
Constraints
Low detection latency, accurate classification, mandatory escalation on SLA breach.
Architecture
Message stream → classification → context enrichment → task creation → escalation by role and deadline.
AI layer
Classification of the signal and its priority; the strong model is used on ambiguous cases, the rest on a cheap one.
Event model
Every incoming message is an event that passes detection, classification and rule-based routing.
Integrations
Mailboxes, channels and the task tracker connected through a normalized layer.
Automation flows
Automatic task creation, role-based routing, escalation as an SLA is approached or breached.
Infrastructure
Event queues, task idempotency (no duplicates per signal), processing timeouts.
Observability
Metrics for response time, classification accuracy and escalation share; you can see where the system erred and why.
Results
Response time and repetitive support workload dropped, fewer signals were lost.
Lessons
Monitoring is an event-driven workflow, not manual mailbox sweeping; the key is low detection latency, not accuracy for its own sake.