AI agent at the medical assistance desk: how Sovcombank automates complex calls
Sovcombank deployed an AI agent at its medical assistance desk. The system helps operators quickly understand the client's issue, record data accurately, and pr
Sovcombank has deployed an AI agent in the work of its insurance company's medical helpdesk. This is not an experimental project to "play with neural networks," but a production deployment of an LLM in a process where the cost of error is high and the client expects a quick resolution.
Where the Agent Works
On the line, an operator hears an insured customer who is explaining their medical problem. The agent listens to the dialogue and helps the operator understand:
- Quickly grasp the essence of the customer's problem
- Correctly enter medical data into the system
- Preserve the context of the dialogue and not lose important details
- Suggest the next step in the workflow
The operator remains in charge: they make decisions and bear responsibility. The agent works like an experienced colleague at their side.
Why This Is More Complex Than Just a Chatbot
In an insurance company's call center, an error can lead to incorrect diagnosis, loss of customer data, or improper handling of an insurance claim. Speed is also critical: a person is waiting for help, and medical consultation requires attention to detail. The agent must simultaneously understand medical terminology, insurance context, data entry requirements, and the customer's stress state. A standard LLM without special training on real cases cannot handle this.
How This Was Implemented
The team started not with full automation, but with operator support. The agent suggests input options, provides hints, and clarifies details based on medical context. Everything goes through the operator — they verify each step.
"The task was not simply to play with neural networks, but to truly embed an LLM into the process," say developers from
Sovcombank.
The setup process included testing on real calls, gathering feedback from operators and doctors, and iterative improvement. To prevent system errors, it was trained on examples of real mistakes and complex cases.
What This Means
AI agents are moving from the category of amusing experiments into the category of critical tools in production processes. If even in insurance, where errors cost money, a company trusts an LLM to help in conversations with customers — it means the technology has matured. The next stage is integrating agents into technical support, legal consulting, and credit assessment.