Turning a grievance queue into a governance instrument
AI and analytics applied across the stages of grievance management — so that leadership sees the pattern behind the volume, not just the volume.
The problem
A state helpline receives citizen grievances at a scale no team can read. Each one is handled, categorised by hand, and routed onward. The system reliably answers how many came in and how fast they closed.
What it cannot answer is the question leadership actually has: what keeps going wrong, where, and which department is the cause rather than the recipient.
What we built
EGM applied AI across the stages of grievance handling — automated categorisation and prioritisation at intake, clustering to identify recurring and systemic issues, and detection of hotspots as they emerge rather than after they escalate.
The same corpus was resolved upward into district- and department-level performance intelligence, delivered to reviewing officers as a dashboard rather than a report pack.
Reach was extended beyond the formal channel: outbound AI-enabled feedback calls, multilingual interfaces, and discovery of grievances surfacing on social media that would never have entered the system at all.
What changed
Grievance handling shifts from reactive redressal — closing what arrives — to proactive citizen intelligence: identifying the systemic fault before the next thousand citizens encounter it.