Case StudiesIn development

Service Operations

An inbound service request becomes a triaged, scheduled, tracked job — with missing information collected before work is committed.

Reference implementation

This workflow has been designed and built as a real, executable engine definition, service-operations-v1. It has not been deployed for a client, and is not presented as client work.

The problem

Service requests arrive as free text across email, phone, and web forms. Someone reads each one, guesses at urgency, chases the customer for details nobody recorded, and schedules work against a calendar held in their head.

How it works today

Request lands in a shared inbox. A coordinator reads it, decides who handles it, emails back for missing details, waits, then creates a job once enough is known. Nothing is tracked until the job exists, so requests stall invisibly.

What the workflow automates

Interpret the request and extract structured operational state

Determine urgency against explicit rules, not model judgment

Identify which required information is missing

Pause explicitly for missing site details — the request waits visibly instead of stalling in an inbox

Create the job and assign an owner once requirements are satisfied

Where humans stay in control

An emergency dispatch requires a manager's explicit confirmation before it proceeds

Emergency classification follows deterministic rules that override normal scheduling

Job creation and owner assignment are recorded in the audit trail with their inputs

Illustrative process comparison

Manual process: 15–30 min · 5 app switches · 4 handoffs
Workflow process: ~2 min · 1 approval · full audit trail

One engine, many businesses

This workflow runs on the same engine as every demo on this site — the same state machine, the same deterministic rules, the same bounded agents, the same idempotent execution. It is a configuration, not a separate application.

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