Web App / AI

Boosting career chances for employees of the financial institution with a recommendation system

Recommendation system

The client is a financial institution that contributes to the growth of developing countries, aiming to reduce poverty. Providing financial products and technical assistance, they help countries share and apply innovative knowledge and solutions to the challenges they face. With offices in over 130 locations around the world, they are one of the biggest financial institutions in the world.

About the project

When the client approached us, they were struggling with a challenge related to their employees’ development. In simple terms, they were searching for a way to help their managers grow, advance in the company structure, or move between positions within the organization.


Back then, the process of choosing courses for their employees was conducted manually. It required a lot of decisions to be made both by the employee and by the manager of a chosen department. To automate this process, the client decided to build an AI-based platform that would recommend the right courses to support the development of their employees and make sure that it aligns with the company’s goals.

Our tasks

Building a Proof-of-Concept to prove value

Before implementing AI, we had to verify if our assumptions about AI performance within the system were right.

Tailoring the platform for different types of users

The AI-powered we planned to develop had to suggest courses not only to employees who wanted to climb the career ladder, but also for those who wanted to move between the positions within the organization.

Personalized courses suggestions with an AI-based platform

We decided to build the recommender system to help managers support company employees in making advances in their careers.



1. Verifying the assumptions as soon as possible

Implementing AI can be a long and costly process. Before spending years on building a complex solution, it is always worth validating the assumptions with a Proof of Concept. The challenge is to find the right area where it is possible to quickly prove the value of such a solution.

2. Meeting the needs of different types of users

While it was crucial for the employees to get good training recommendations, at the same time it was important for the managers to make sure that their teams have the right skill set and that the courses help them get the missing skills. Also, it was important to consider that the employees may want to advance not only to a higher level of their current roles but also to different positions.

3. Understanding the current process of assigning the courses

Every corporate process has its business logic and it’s important to understand it as soon as possible as it helps align the product with that process and make sure that no part of that process is missing.

Most exciting part of the AI involved, was to tackle diversity of offered content.
Training materials for users are made in many different languages, and teaching about different problems. Whole project showed that, project should not only work flawlessly, but should look great !
Michał Trojnarski Photo

Michał Trojnarski / AI Engineer



1. Neoteric AI Sprint

We conducted 2-day workshops – the Neoteric AI Sprint – with our team, and made a Proof of Concept of a model that was chosen during the workshops. The goal was to identify the right path to focus on in order to show the value of implemented models as quickly as possible. The Proof of Concept, which is the final product of the Sprint, is a real-life model trained on the historical data, which proves the idea of using AI to solving a specific business case.

2. Building predictive models

Fed with historical data showing which courses were taken by different employees and how they impacted their careers, basing on similarities between the courses and different users, the predictive models we built are able to suggest the right courses to support career advancement within the structures of the institution. Depending on the goals of the employee and the assessment of skills needed in every team, the models are able to suggest the right courses that make the training process more effective.

3. All-round approach

Even though the core of the project was about building and training predictive models that would recommend courses to employees, creating a good Proof of Concept required combining skills from different areas as well. In order to make sure that our Proof of Concept is scalable, we used serverless architecture for its backend part. As we wanted to provide the client with a usable platform, we involved a UI designer and a frontend developer to take care of the visual part of the project.

Technology we used

Project Results

PoC built in less than 3 months

From the very beginning, it was important to validate the idea of implementing AI as quickly as possible. After the AI Sprint workshops, we agreed on the problem that we wanted to solve with predictive models and on the project scope. The next step was to deliver a product that would prove to be a solution to that problem that is worth investing in. The Proof of Concept that we’ve built using the data provided by the client has met their expectations, being able to suggest the right course recommendation for their employees. As the models can learn with every new data record they get, their suggestions are more and more accurate every day.

Hours saved by
streamlining the

The platform was able to streamline the process that previously required a lot of decisions to be made both by the employee and by the manager of a chosen department. Now, the platform is able to suggest the right courses that would support managers in growing their dream teams and help employees advance their careers. The managers are able to see their teams’ development in a visual way and quickly assess what skills are missing, while employees get course suggestions as a clear list of recommendations – just like the way they get movies recommendations on Netflix.


The client received a Proof of Concept that equipped employees with personalized courses and supported their career development. With more employees using the platform over time the system will increase its accuracy of recommendations.

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