AI Agents & Workflows
Facilities Management Company
Helpdesk email analyst on a 48-column schema.
An AI Email Analyst Agent that processes every inbound and outbound helpdesk email against a 48-column schema, flagging SLA risk, churn risk, complaint severity, and FAQ candidates, with a call analyst running in parallel.
Why it matters
A busy facilities helpdesk runs on email, and volume hides the things that hurt most: a slipping SLA, a customer about to churn, a complaint buried in a polite message. Read manually, tickets get missed and sentiment goes untracked, so nobody has a real picture of what is coming in and how urgent.
Our agent processes every inbound and outbound email against a 48-column schema covering routing, sentiment, risk, job lifecycle, and knowledge base candidates. It flags SLA risk, churn risk, and complaint severity as they appear. A call analyst runs alongside to guard against disputed dropped calls. You get an early-warning system, not a backlog to sift through.
The problem
A large facilities management company processed heavy volumes of helpdesk email manually. Tickets were missed, sentiment was untracked, complaints went undetected, and there was no structured view of what was coming in, from whom, and how urgent.
How it works
The agent processes every inbound and outbound helpdesk email against a 48-column schema.
The schema spans identity, routing and classification, content and action, sentiment and risk, job lifecycle, commercial signals, and knowledge base candidates.
It flags SLA risk, churn risk, complaint severity, escalation priority, and FAQ candidates.
All data lands in a structured Airtable base.
A call analyst agent runs in parallel to monitor call performance and protect against disputed dropped or missed calls.
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