{"id":125,"date":"2026-10-06T18:59:43","date_gmt":"2026-10-06T18:59:43","guid":{"rendered":"https:\/\/blog.forfis.com\/blog\/austrian-insurtech-voice-agent-rag-pilot\/"},"modified":"2026-10-06T18:59:43","modified_gmt":"2026-10-06T18:59:43","slug":"austrian-insurtech-voice-agent-rag-pilot","status":"publish","type":"post","link":"https:\/\/blog.forfis.com\/blog\/austrian-insurtech-voice-agent-rag-pilot\/","title":{"rendered":"Austrian Insurtech Cuts Support Cycle Time 50% with Voice Agent and RAG Pilot"},"content":{"rendered":"

Background: A 300-Person Austrian Insurtech<\/h2>\n

This case study is a composite based on patterns observed across multiple engagements. We do not name real customers. The company described here matches the profile of a mid-sized Austrian insurtech: 300 employees, 12 years in operation, serving private and small-business customers across Austria and Germany. The stack includes a legacy CRM (Salesforce), an ERP (SAP), and a helpdesk (Zendesk). Internal documentation lives in Confluence, with some policy procedures in Notion. The company had been using basic rule-based chatbots for two years but had not moved to generative AI. The operations team was under pressure to scale support without adding headcount, as the Austrian labor market for customer support specialists was tight and salaries had risen 12% year-over-year.<\/p>\n

Challenge: Scaling Support Without New Hires<\/h2>\n

The operations director identified three specific pain points. First, 45% of inbound support tickets involved repetitive data entry: policy number lookups, claim status updates, and address changes. Second, agents spent an average of 14 minutes per ticket searching internal documentation for policy details and claim procedures. Third, the company faced a compliance deadline under the EU AI Act, which required transparency and human oversight for customer-facing AI systems. The deadline was 18 months out, but the company wanted to be ahead of the curve. The operations team had 12 full-time support agents, and the director was told by HR that hiring two more would cost EUR 120,000 annually. The goal was to replace manual data entry and reduce documentation search time without adding headcount.<\/p>\n

Approach: Process Audit and Fixed-Scope Pilot<\/h2>\n

The engagement began with a two-week process audit. We mapped every step of the top 20 support workflows, measured cycle time and error rate for each, and identified where manual data entry occurred. The audit revealed that 60% of the top 20 workflows involved repetitive data entry that could be automated. We then built a fixed-scope pilot targeting one workflow: first-response triage for policy status inquiries. The pilot used Anthropic Claude API for the voice agent, with a RAG assistant indexing Confluence and Notion documentation. The architecture was model-agnostic, so we could switch to an open-weight model on the client\u2019s hardware if data residency became an issue. The pilot integrated with Salesforce and Zendesk through their APIs, not by replacing them. Human-in-the-loop approval was built in: the voice agent drafted responses and extracted data fields, but a human approved anything that touched money, health data, or a contract.<\/p>\n

Outcome: Measured Baseline and Rollout Decision<\/h2>\n

The 8-week pilot delivered measurable results. Average ticket resolution time for policy status inquiries dropped from 14 minutes to 7 minutes, a 50% reduction. Manual data entry errors fell from 8% to 2%, a 75% reduction. The voice agent handled first-response triage for 70% of policy status inquiries, reducing the need for human escalation. The RAG assistant cut documentation search time from 14 minutes to 3 minutes per ticket. The human-in-the-loop approval process added 2 minutes to each ticket, but the net effect was a 5-minute reduction in cycle time. The pilot met the EU AI Act transparency requirements: all interactions were logged, and the voice agent disclosed its AI nature to customers. The operations director approved a rollout to the remaining 19 workflows, with a target of 12 months for full deployment.<\/p>\n

Lessons for Similar Teams<\/h2>\n