Smarter AI-powered Information Search for Better Care – Sentina Oy

Why this project?

Care work depends on timely and trustworthy information. Traditional search solutions do not always meet the urgent and demanding needs of care environments. Sentina wants to explore how AI can make information retrieval more efficient, natural and human-centered-improving the quality of care while reducing workload.

Company Introduction

Sentina Oy is a Finnish company that develops digital health solutions. Its mission is to make care work easier and improve the quality of care by applying technology in a practical and ethical way.

What are we doing in the project

Developing and validating a new AI-assisted smart for care professionals.
Developing a search tool that interprets user intent to deliver more relevant and human-friendly results.
Prioritizing accessibility, ease of use and ethical responsibility in the solution.
The solution is tested and refined based on feedback and measurable impacts.

Connection to AI skalaajaat

The project is part of the AI skaalaajat -project, which supports SMEs in Uusimaa by providing expert services under the de minimis aid scheme. Parallel experiments with other companies enabled faster learning and scalability. The results will benefit not only Sentina but also SMEs more broadly in social and healthcare services.

Results/Benefits

Through the project Sentina Oy is building long-term expertise in applying AI to care work. The results improve employees' daily work, support high-quality care and provide a model for other SMEs in the responsible adoption of AI.

Initial Mapping and Current State Assessment

In the first phase of the project, the current AI capabilities of the participating SMEs are assessed. This includes:

Defining the companies' AI-related goals and needs.

An AI maturity analysis to evaluate the company's readiness and capabilities to utilize AI in business.
Identifying the companies' business environment and technological challenges.

The goal is to form a clear picture of the companies' starting point and define the target state to be achieved with AI solutions.

AI Coaching and Workshops

In the second phase, companies participate in iterative training sessions and workshops. During these, the focus is on the following topics:

AI fundamentals and opportunities: How can AI create added value?
Proof of Concept (POC): Companies are provided with a concrete model for utilizing AI in a small-scale pilot project.
Multidisciplinary capabilities: How to combine expertise and technologies from different fields with the help of AI?

The workshops support companies in ideating and testing concrete AI solutions, while also providing an opportunity to network with other companies and experts.

Solution Concept Development and PoC Projects

At this stage, we move on to company-specific PoC projects, in which:

• The first AI solution models are put into practice in company business environments.

• The developed concepts are tested and validated in practice.

• Collaboration and expert support are utilized.

Minimum Viable Product (MVP): Each company will have a basic version of an AI solution developed, serving as the minimum viable product (MVP) to allow efficient testing before large-scale implementation.

Development of the Overall Concept and Scaling

At this stage, the lessons learned from the PoC projects are integrated into a tailored 'Whole AI Service' concept for each company, which includes:

Processes and models needed for the implementation and management of AI solutions.
Strategies for utilizing multidisciplinary networks.
Guidelines for scaling and integrating technologies into business processes.

The concept provides companies to scale and utilize AI solutions effectively in the long term.

Co-creation and Sharing Results

The last phase of the project highlights co-creation and learning:

Companies share the experiences gained during the PoC projects within their networks.
Workshops are organized for companies and experts to develop solutions together.
The project results are communicated and best practices are shared more broadly.
At the same time, future development opportunities are identified and it is ensured that the participating companies can continue to utilize AI solutions after the project ends.