Automation of Customer Service Tickets with S-PRO AI Agent Plugin
Our AI Agent Plugin transform your customer support operations through intelligent AI agents that classify tickets, retrieve data from multiple backend systems, and generate compliant responses — all within their existing ticket system environment.
Applicable Industries: Banking & Neobroking | Asset Management | Insurance | E-Commerce | Telecommunications | SaaS Platforms
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Key Performance Metrics
Results reported for this 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. A person signs off on every AI draft before it is sent.
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.
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
AI Agent for Customer Support:
Leading German Neobroker
The Challenge
- Rapid user growth demanded a scalable, compliant solution — immediately
- Regulatory pressure: every interaction must comply with GDPR and the EU AI Act
- Growth bottleneck: support team fully occupied, no capacity for new service channels
The Solution
- AI Agent deployed directly into the existing ticketing environment — no disruption to existing workflows
- Domain-specific AI responses pulling real-time data from the data warehouse
- Human-in-the-loop approval required before any AI response reaches the customer
Business Impact
- New channel unlocked Freed agent capacity enabling premium phone support for higher customer satisfaction
- Built to scale System grows with the user base — no proportional headcount increase required
The Results
Measurable outcomes from live production deployment. AI support agent for a leading German neobroker
Questions buyers ask us
What does an AI support agent do in Zendesk?
It classifies each incoming ticket, retrieves customer context from backend systems and drafts a personalised reply. An operator reviews the draft and sends it.
Does a person approve the replies?
Yes. In both deployments described here, a person signs off on every AI draft before it is sent.
What results have you seen?
For a leading German neobroker, our AI support agent reached 90% correct ticket classification; in 85% of cases its draft reply was not wrong, and ticket handling became more than 40% faster. A support agent approves every draft.
Which models does the agent run on?
The wealth-management platform deployment uses Anthropic's models through Amazon Bedrock. S-PRO is model-agnostic.
Can it work with our ticketing system?
Both deployments run inside the client's existing Zendesk environment; other systems are assessed in discovery.
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