What does a four-week pilot look like for a 15-person fintech?

For a 15-person fintech, the pilot scope is one workflow: order and shipment status updates. The team maps the current manual process, identifies the data sources (ERP, carrier APIs, CRM), and defines the success metrics. The pilot runs for two weeks with a human-in-the-loop approval step for any response that touches a customer's financial data. The goal is to measure cycle time reduction and error rate before scaling to other support channels.

How does ISO 27001 compliance affect the voice agent deployment?

ISO 27001 requires documented controls for data access, encryption, and audit logging. The voice agent must log every interaction, encrypt data in transit and at rest, and restrict access to customer records. The OpenAI API must be configured to not store training data, and the client's own infrastructure must handle any data that cannot leave the building. The dedicated AI team handles the technical implementation, while the client's compliance officer reviews the documentation.

What happens when the voice agent encounters a complex issue?

The voice agent handles the first 70-80% of status inquiries, which are repetitive and data-driven. The remaining 20-30% involve complex issues (disputes, refunds, contract changes) that require human judgment. The human-in-the-loop model ensures that any response touching money, health data, or a contract is approved by a person before it reaches the customer. This keeps the agent useful without creating compliance risk.

How does the voice agent integrate with existing systems?

The custom REST API and webhooks connect the voice agent to the client's ERP, CRM, and carrier systems. The agent queries the ERP for order status, the CRM for customer history, and the carrier API for shipment tracking. The webhooks push updates back to the client's systems when the agent takes an action. This integration is built on the client's existing infrastructure, not a replacement, so the client retains control over their data and systems.

What is the ROI of the voice agent for a 15-person fintech?

The voice agent reduces the time a support agent spends on status inquiries from 5-7 minutes to 30-60 seconds. This frees up the agent to handle complex issues, which improves customer satisfaction and reduces churn. The agent also provides a consistent, accurate response, which reduces errors and rework. The before/after baseline measures these improvements and provides the data to justify scaling the automation to other workflows.

Who is responsible for the voice agent deployment?

The dedicated AI team handles the technical implementation, including the process audit, pilot design, and deployment. The client's team provides the domain knowledge, compliance requirements, and access to their systems. The team works with the client's compliance officer to ensure the deployment meets ISO 27001 requirements. The client's team also provides the human-in-the-loop approval step, which is critical for any response that touches financial data.

Can the voice agent use different AI models?

The voice agent is model-agnostic, which means it can use OpenAI and Anthropic APIs where quality matters, and open-weight models on the client's own hardware where regulated data cannot leave the building. This flexibility ensures that the agent can meet the client's compliance requirements without sacrificing performance. The dedicated AI team selects the right model for each use case, based on the client's data sensitivity and performance needs.

Forfis Blog

Austrian Fintech Automates Order Status with a Voice Agent in Four Weeks

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Forfis

Fintech and Payments

The Support Team Is Drowning in Status Inquiries

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.

Why Existing Solutions Fall Short

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’s 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’s existing systems or meet its compliance requirements.

A Voice Agent That Integrates With Existing Systems

The proposed approach is a voice agent that integrates with the company’s existing ERP, CRM, and carrier APIs. The agent uses the OpenAI API to understand the customer’s 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’s 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’s systems, so the agent can query and update data in real time.

How to Start: Four Concrete Steps

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’s 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.

Austrian Fintech Automates Order Status with a Voice Agent in Four Weeks

The Support Team Is Drowning in Status Inquiries

Why Existing Solutions Fall Short

A Voice Agent That Integrates With Existing Systems

How to Start: Four Concrete Steps

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