From a free-text message to a structured record
A complaint may arrive through a form, email, app or service desk. AI can summarise the message, identify its subject, extract location references and suggest a category while preserving the original text.
The system must not erase ambiguity. When information is missing or several interpretations are possible, it should ask for clarification or send the case to human triage.
Classification and routing
AI suggestions can be combined with territorial rules, departmental responsibilities and urgency levels defined by the municipality. Explicit rules should prevail whenever an objective criterion exists.
Every routing decision must be correctable. Corrections should inform evaluation without automatically turning every interaction into training data.
Preparing the response
AI can prepare a draft based on case status and approved templates. It should not make commitments that the responsible service has not confirmed, and it should explain the next step clearly.
Sensitive, repeated, urgent or potentially contentious cases should be reviewed by a person before any response is sent.
Indicators and control
Measure time to triage, category corrections, transfers between services, requests lacking sufficient location data, response deadlines and reopened cases. These measures show whether the process improved for citizens, not merely whether it produced more messages.
Frequently asked questions
Does AI decide which department is responsible?
It can suggest a department from the content and rules, but correction and escalation must remain available for ambiguous or sensitive cases.
Must every location be identified automatically?
No. A location can be extracted when provided, requested from the citizen or confirmed by a person.
How can incorrect responses be reduced?
Use approved sources, response templates, autonomy limits, human validation and tests based on real cases and exceptions.
Architecture: AI for complaint handling in local government →