Customer complaints are often buried in emails, forms, or support tickets and handled inconsistently—leading to poor resolution times and dissatisfaction. Manually triaging and resolving complaints is slow and prone to errors. This template enables automated intake, classification, prioritization, resolution, and escalation of customer complaints using coordinated AI agents within the Syncloop platform, ensuring fast, fair, and traceable handling.
Multiple AI agents work together to process complaints from different channels, analyze their nature and severity, classifythem, and trigger either automated or assisted resolution flows. Each agent is assigned a role—ingestion, sentiment analysis, category detection, SLA assignment, response generation, and escalation handling. Agents interact via Syncloop APIs, enabling streamlined complaint resolution pipelines.
| Endpoint | Method | Input Parameters | Output Format |
|---|---|---|---|
| /complaint/intake | POST | channel, message_text, contact_info, timestamp | complaint_id, source, cleaned_text |
| /sentiment/analyze | POST | complaint_id, cleaned_text, channel | sentiment_score, emotion_tag, urgency_flag |
| /complaint/classify | POST | complaint_id, cleaned_text, sentiment_score | category, subcategory, classification_confidence |
| /sla/assign | POST | complaint_id, category, urgency_flag, customer_tier | sla_hours, priority_level, assigned_team |
| /response/generate | POST | complaint_id, | message_body, tone, |
| /escalation/manage | POST | complaint_id, resolution_status, sla_breach_flag, tier | escalation_path, assigned_supervisor, alert_triggered |
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A scalable multi-agent AI system tailored for an American restaurant specializing in Mexican cuisine, orchestrated on Syncloop.
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