Terveystalo: A customer service solution leverages AI and data for faster and more efficient information retrieval
Nero helps customer service agents find the right information at the right time, improving the experience for Terveystalo employees and customers.

Terveystalo builds a humane and functional work environment where its experts can focus on meaningful matters. They develop digital solutions that ensure a smooth everyday life for professionals, the best care outcomes for customers, and help address the care gap in the industry.
Service agents are vital in ensuring a quality experience for Terveystalo’s customers. Terveystalo’s customer service agents handle approximately 2.5 million customer calls annually, often requiring access to dozens of systems and tools to find contract information, locations, and the right experts.
Vision: Achieving higher employee satisfaction and improved information retrieval with a system that functions efficiently under heavy user volumes, as more than 400 agents might be searching for information simultaneously.
Terveystalo is Finland’s largest private healthcare provider in terms of revenue and network. It is also the leading player in occupational health services in the Nordics. In 2023, Terveystalo served 1.2 million individual customers in Finland, with approximately 7.6 million visits. Terveystalo employs over 15,500 health and wellness professionals.
Customer service relies heavily on experienced employees familiar with complex systems and information retrieval environments. Much of this expertise is implicit, acquired through years of experience. Training new employees can take months, impacting efficiency, adding costs, and lowering employee and customer satisfaction.
Customer service includes verifying contract and payment details, which requires using multiple tools and sources to access accurate and up-to-date information. Agents frequently handle information related to locations and experts, such as doctor profiles, office locations, hours of operation, and accessibility details. All this information needs to be provided quickly and reliably.
Challenge: High call volumes combined with information distribution across dozens of systems made customer support difficult and inefficient.
The project was carried out in two phases. In the first phase, a four-week proof-of-concept (PoC) trial, we validated that the planned solution would ease the work of customer service agents. In the second phase, we expanded the solution to include additional data systems and created a version suitable for production.
We collaborated closely with customer service agents using an agile development model, collecting feedback at each project stage. In partnership with Terveystalo's agents, we used this feedback to continually refine and improve the application.
During the first phase, we focused on understanding the scope of the problem and selecting the most critical use cases. We initially identified the tools and information sources most frequently used by customer service agents. Within two weeks, we developed the first version of an AI solution called Nero, which agents could immediately begin testing. An active group of agents used the tool in real work situations and provided feedback to the development team. Simultaneously, we conducted a business case analysis to estimate the potential benefits and costs, supporting Terveystalo’s investment decision.
Nero expanded Terveystalo's search capabilities, allowing agents to retrieve information from systems previously entirely lacking search functionality. We made the new search tool easy for personnel to adopt by making it operate in a familiar manner. We also developed more sophisticated AI-powered tools that agents could use concurrently with the traditional search methods. The AI search understood context and enabled queries in natural language. It organized unstructured source data to improve search functions. For example, when an agent searches for an 'active contract,' the system can immediately differentiate it from inactive contracts. Previously, this would have required multiple searches or manual checks.
Throughout the project, we focused heavily on helping agents transition to the new information retrieval method, bringing them into the development process and providing thorough training on the tool.

In the second phase, we expanded the services by adding new data sources in collaboration with Terveystalo's data team. The updated application needed to provide broader and more precise searches quickly and accurately, even with hundreds of concurrent searches. This required a careful consideration of how large language models (LLMs) could be utilized to maintain the application's speed, accuracy, and cost-effectiveness. Given the high volume of searches, using LLMs to process search results wasn't practical — instead, we employed pre-processing to structure natural language information, thereby simplifying the search process.
Throughout the project, we collaborated with Terveystalo's architects to design a scalable AI architecture and built the production environment in Microsoft Azure. This architecture offers a cost-effective, centralized method to manage AI resources within Terveystalo's cloud environment. It facilitates the easy addition of new data sources and has already been used to develop applications for new use cases using existing resources.
Terveystalo has a highly developed cloud environment and substantial internal expertise in cloud technology. This allowed the Nero project to be executed swiftly and efficiently by leveraging existing practices and integrating the new AI solutions into Terveystalo's overall architecture.
The final solution is a browser-based Nero search tool that allows agents to retrieve information from multiple sources simultaneously. Previously, agents had to use several different systems and search for information manually during calls. Now, a single search retrieves information from multiple places, with all results displayed in one place.
Technologies used
- Natural Language Processing (NLP)
- Large Language Models (OpenAI)
- Advanced Prompt Engineering
- Entity Recognition
- Structured Information Extraction
- Azure AI Search
- Azure OpenAI
- Unified Data Model/Schema
- Python
RESULTS: AI-enabled access to data has cut customer service search times to half or even one-third of the previous.
The Nero search tool was launched in the fall of 2024, and agent feedback has been positive. The solution has significantly streamlined and improved customer service work. Agents especially appreciate being included in the development process from the beginning.
Nero consolidates information from various sources into a single, user-friendly platform, enabling efficient and intelligent search capabilities. The designers collaborated closely with customer service agents to ensure the solution met their needs. As a result, agents quickly adopted the new tool, significantly improving their efficiency and overall speed of customer service.
By timing one thousand calls first in May 2024 and then in December of the same year, we were able to determine that Nero has had a significant impact on the speed of information retrieval:
- Occupational health customers: time spent decreased by 22%, i.e. approximately eight seconds.
- Private customers: time spent decreased by 32%, i.e. approximately 13 seconds.
In November, the average length of a customer support call was 3:21 and currently time spent on information retrieval averages approximately 30 seconds. Given that agents handle about 2.5 million customer calls annually, the optimized search saves significant work time.
I think it's great that the people who have struggled the most with finding information will see the biggest benefit from Nero. While some searches still require verification with old tools, the change will be even bigger as we fully transition to Nero. I’ve really enjoyed being part of this project—it’s been so interesting! The Futurice team also mentioned several times how great it was to develop Nero directly with end-users, as it gave them a fresh perspective.
Grandone details
- Työ (Work): Nero AI
- Asiakas (Client): Terveystalo
- Suunnittelu (Design): Futurice, Terveystalo Brändi- ja kuluttajamarkkinointi
- Tuotanto (Production): Futurice, Terveystalo: DAR-team, Platforms & Networks - team, asiakaspalvelukeskus, asiakaspalvelu
- Kategoriat (Categories): Paras AI:n tai muun uuden teknologian käyttö & Paras datan käyttö (Best use of AI or other new technology & Best use of data).