Automation of Customer Service Tickets with Agentic AI
How we helped Germany's largest neobroker transform their customer support operations through intelligent AI agents that classify tickets, retrieve data from multiple backend systems, and generate compliant responses — seamlessly integrated into existing ticketing systems and adaptable to any workflows.
Applicable Industries: Banking & Neobroking | Asset Management | Insurance | E-Commerce | Telecommunications | SaaS Platforms
Get Your SolutionKey Performance Metrics
Measurable impact delivered within the first 12 months of full deployment.
How It Works
Ticket Ingestion & Classification
Every incoming support ticket is automatically ingested from ticketing system. The AI agent applies multi-label classification to identify intent, topic category, urgency, and regulatory sensitivity — building a structured understanding of each request before any response is drafted.
Multi-System Backend Data Retrieval
Once classified, the agent queries multiple internal backend systems — including account management, transaction history, compliance records, and product databases — to gather all relevant customer context. No manual lookups required by the support agent.
Compliant Response Drafting
Using retrieved data and classification output, the AI generates a fully contextualised, regulation-aware draft response — adhering to the neobroker's tone-of-voice guidelines, BaFin-relevant disclaimers, and internal compliance rules — directly within the ticketing interface.
Human-in-the-Loop Review & Send
The human support agent reviews the AI-generated draft with full context surfaced alongside it. They can approve with a single click, make minor edits, or override entirely. This hybrid model achieves the 95% acceptance rate while keeping compliance control firmly with the human team.
Business Approach & Ticket Taxonomy
The foundation of this solution is a carefully engineered taxonomy of customer support request types — built through discovery workshops with the client's operations and compliance teams. Each resolution type drives a distinct AI behaviour, from full auto-draft to human escalation.
Taxonomy-First Design: We mapped 40+ ticket intents across 8 categories before writing a single line of AI logic, ensuring coverage of the real distribution of inbound queries.
Regulatory Guardrails: Every auto-drafted response passes through a compliance filter tuned to BaFin requirements and MiFID II obligations, with mandatory disclaimer injection where required.
Escalation Logic: Tickets flagged as sensitive (complaints, fraud suspicion, account closure) are immediately routed to a senior agent with a full context brief — no AI draft is shown.
Continuous Feedback Loop: Agent edits and overrides are logged and fed back into the model evaluation pipeline, enabling monthly accuracy improvements without retraining from scratch.
| Resolution Type | AI Role | Volume Share |
|---|---|---|
| Account Information | Full AI Draft | ~35% |
| Transaction Status | Full AI Draft | ~25% |
| Product Explanation | AI Draft + Review | ~15% |
| Regulatory Inquiry | AI Context + Human | ~12% |
| Complaint / Escalation | Human Only | ~8% |
| Fraud Suspicion | Immediate Escalation | ~5% |
Technical Architecture & Integration
Built natively within the client's existing Zendesk environment using a modular agentic architecture. No replacement of existing tooling — only intelligent augmentation. The system runs as a Zendesk App backed by a dedicated AI middleware layer.
Zendesk-Native Integration: Custom Zendesk App using the Apps Framework v2. The AI panel surfaces inline with the existing ticket view — zero workflow disruption for agents.
Agentic Middleware: Python-based orchestration layer using a structured tool-calling loop to query internal APIs, apply business rules, and compose the final response payload.
Secure API Gateway: All backend calls (account data, trade history, portfolio) route through an internal API gateway with field-level access control — the AI model never directly touches raw production databases.
Observability & Audit Logging: Full audit trail for every AI draft generated — model version, retrieved data snapshot, agent action taken — stored for 10 years to satisfy regulatory retention requirements.
| Requirement | Solution |
|---|---|
| AI Model | Hosted LLM (private deployment, EU data residency) |
| Backend Integrations | REST APIs: Core Banking, Portfolio, CRM, Compliance DB |
| Latency Target | < 4 seconds per draft generation (p95) |
| Data Residency | EU-only, GDPR-compliant, no data leaves German servers |
| Authentication | OAuth 2.0 + role-based access control (RBAC) |
| Audit Logging | Immutable log per ticket, 10-year retention |
| Availability | 99.9% SLA, auto-fallback to human-only mode |
Implementation Roadmap
Ready to Transform Your Customer Support?
- Audit of your current support ticket distribution and volume
- Identification of the highest-ROI automation candidates in your backlog
- Assessment of your backend system readiness for AI integration
- Compliance and regulatory requirements review for your industry
- Tailored roadmap proposal with realistic ROI projections for your context