A 15-person fintech in Austria handles 300-500 customer support tickets per week. The majority are order and shipment status inquiries. Each inquiry takes a support agent 5-7 minutes to resolve: they check the ERP for order status, the CRM for customer history, and the carrier API for shipment tracking. The agent then drafts a response, reviews it for accuracy, and sends it. This process is repetitive, data-driven, and error-prone. The support team is stretched thin, and the company cannot hire more agents without breaking the budget. The pain is not a lack of technology; it is a lack of time and bandwidth to handle the volume of routine inquiries.<\/p>\n
The company has tried two approaches. First, they built a custom chatbot using a rule-based system. The chatbot handles simple inquiries but fails on complex ones. It cannot query the ERP or CRM in real time, so it provides outdated or inaccurate information. Second, they considered a generic AI chatbot. The chatbot can draft responses, but it lacks the context to handle the specific data sources the company uses. It also cannot meet the company\u2019s ISO 27001 compliance requirements, because it stores data in the cloud and does not provide the audit logging the company needs. Both approaches fail because they do not integrate with the company\u2019s existing systems or meet its compliance requirements.<\/p>\n
The proposed approach is a voice agent that integrates with the company\u2019s existing ERP, CRM, and carrier APIs. The agent uses the OpenAI API to understand the customer\u2019s inquiry and draft a response. It queries the ERP for order status, the CRM for customer history, and the carrier API for shipment tracking. The agent then sends the response to the customer. The human-in-the-loop model ensures that any response touching financial data is approved by a person before it reaches the customer. The agent is deployed on the company\u2019s own infrastructure, which meets the ISO 27001 requirements for data access, encryption, and audit logging. The custom REST API and webhooks connect the agent to the company\u2019s systems, so the agent can query and update data in real time.<\/p>\n
The first step is a process audit. The dedicated AI team maps the current manual process, identifies the data sources, and defines the success metrics. The second step is the pilot design. The team selects one workflow (order and shipment status updates) and defines the scope, timeline, and success criteria. The third step is the pilot deployment. The team builds the voice agent, integrates it with the company\u2019s systems, and runs the pilot for two weeks. The fourth step is the measurement. The team measures the cycle time and error rate before and after the pilot. The fifth step is the rollout. If the pilot meets the success criteria, the team scales the automation to other support channels.<\/p>\n","protected":false},"excerpt":{"rendered":"
A 15-person Austrian fintech automates order and shipment status updates with a voice agent in four weeks. The pilot uses OpenAI API, custom REST APIs, and human-in-the-loop approval to meet ISO 27001 requirements.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"Austrian Fintech Automates Order Status with a Voice Agent in Four Weeks","rank_math_description":"A 15-person Austrian fintech automates order and shipment status updates with a voice agent in four weeks. 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