AI and automation case study
AI-Driven Claims & Complaint Resolution System
An AI-driven claims and complaint resolution system connected CRM, ERP and warehouse operations for faster, more consistent decisions.
Project at a glance
- Client profile
- Mid-size UK retail and distribution business
- Engagement
- Claims and complaint automation
- Architecture
- API-led, event-driven workflows
- Outcome
- Scalable resolution with human oversight
Business context
Challenge
A mid-size UK retail and distribution business needed to automate and standardise the handling of customer complaints and order discrepancies. Complaint resolution depended on manual investigation across CRM, ERP and warehouse systems, with inconsistent decisions, limited auditability, and heavy reliance on experienced staff during peak periods.
Delivery response
Solution
MPED designed and implemented an AI-driven operational model for complaint handling, combining advisory, architecture, integration and implementation. The solution connected CRM, ERP, WMS, email and internal knowledge sources through an API-led layer, added agent-based decision workflows, and introduced controlled validation and escalation paths so routine cases could be resolved faster without losing human oversight.
From intake to resolution
Delivery scope
The workflow combined complaint intake, cross-system context, controlled AI support and escalation into one traceable operational path.
- Automatic intake and classification of complaints
- Context building by aggregating data from CRM, ERP, WMS and communication systems
- AI-supported decision-making based on predefined business rules
- Automated response generation for standard cases
- Triggering returns, replacements and corrections
- Escalation to human operators when policy or confidence thresholds require review
- Full audit trail of decisions and actions
Operational use
Applications
The delivery supported routine complaint handling while keeping exception paths visible and controlled.
- Customer complaint resolution
- Order discrepancy handling
- Returns and replacement processing
- Operational exception handling
Engineering detail
Technical specifications
The architecture separated execution, validation and persistent state so AI-supported actions remained traceable.
- Event-driven architecture using message queues
- Stateless execution of AI-driven workflows
- API-first integration across complaint, order and fulfilment systems
- Separation of execution and validation layers for controlled AI decisions
- Persistent state tracking for continuity, traceability and audit readiness
Operational impact
Results
- Significant reduction in manual processing effort
- Faster response times to customer complaints
- Improved consistency in decision-making
- Reduced dependency on senior staff for routine case resolution
- Increased process scalability without proportional headcount growth
- Better control, visibility and auditability across complaint handling
Summary
Structured complaint handling without uncontrolled automation
The AI-driven claims and complaint resolution system gave the client a structured, scalable process for handling complaints and order discrepancies across retail and distribution operations. By combining automated data gathering, controlled AI decision support and clear escalation logic, MPED helped reduce manual workload, improve response consistency and strengthen auditability without exposing the business to uncontrolled automation.