Automated Classification, Routing and SLA-Risk Prediction
Client context
A large municipal corporation received citizen complaints through multiple channels, including mobile applications, web portals, call centres, control rooms and departmental systems.
The complaints covered a wide range of civic issues, such as roads, sanitation, drainage, streetlights, water supply, encroachments and public health.
The challenge
Citizen grievances frequently contained incomplete, unstructured or ambiguous information.
Each complaint had to be interpreted and assigned to the correct department, category, location and responsible officer.
This created several operational challenges:
- Incorrect classification resulted in complaints being transferred between departments
- Manual routing delayed the start of resolution
- Similar complaints were registered repeatedly
- High-risk or urgent complaints were not always identified early
- Officials had limited visibility into complaints approaching their SLA deadlines
- Department performance was measured after delays had already occurred
- Complaint descriptions written in different languages were difficult to process consistently
- Static reports showed status but did not explain emerging operational risks
Our approach
We developed a Grievance Intelligence module that analysed incoming complaints and enriched them with operational intelligence.
The solution used natural-language processing and configurable municipal rules to understand complaint descriptions, predict the appropriate category and recommend the responsible department.
It also monitored the complaint lifecycle to identify cases at risk of breaching service-level commitments.
The intelligence layer was designed to work alongside the corporation’s existing grievance-management system.
Solution components
- Integration with existing grievance applications and databases
- Scheduled ingestion of new and updated complaints
- Complaint-text cleaning and preprocessing
- Automated category and subcategory classification
- Department and officer-routing recommendations
- Priority and urgency identification
- Multilingual complaint analysis
- Duplicate and related-complaint detection
- SLA-risk scoring
- Ageing and pending-grievance monitoring
- Department-level dashboards
- Exception queues for human review
Key intelligence capabilities
- Predicting the most relevant complaint category
- Recommending the responsible department
- Identifying incorrectly routed complaints
- Detecting urgent or sensitive complaints
- Flagging complaints approaching SLA deadlines
- Identifying long-pending and repeatedly transferred cases
- Detecting geographic or thematic complaint clusters
- Highlighting repeat complaints from the same area
- Comparing resolution performance across departments
- Generating daily action lists for responsible officials
Operational value
- Reduced the manual effort required for first-level complaint triage
- Improved consistency in complaint classification
- Supported faster routing to the appropriate department
- Provided early visibility into potential SLA breaches
- Helped departmental officers focus on complaints requiring immediate action
- Improved monitoring of ageing, transfers and pending cases
- Enabled management teams to identify recurring civic problems
- Created structured data from unstructured citizen descriptions
Strategic value
The module shifted grievance management from retrospective reporting to proactive intervention.
Instead of only showing how many complaints had already breached their SLA, the system helped officials identify which complaints were likely to breach and where preventive action was required.
Capabilities demonstrated
Natural-language processing, classification systems, workflow intelligence, SLA-risk prediction, multilingual AI, municipal operations and human-in-the-loop automation.