The client is a start-up operating in the mental health area, leveraging cutting-edge neuroscience, linguistics, psychology, and effective Machine Learning. During our cooperation, they were building a platform for a depression diagnosis.
Initially, the client reached out to us looking for front-end development support to build the platform’s very early version. At this stage, we worked with them to build a solid AI-ready MVP. They were also looking for a tech partner to broaden their front-end development skills. In the end, the client returned to us three more times, seeking our support on the project’s next steps.
Creating the front-end part of the platform, a scalable set of functionalities for its basic version (including log-in/registration process, consent granting, patient dashboard, embedding tasks for users, etc.).
Building a solid MVP of the web application that would allow the client to demonstrate its functionality to the stakeholders.
As some of the project elements were not part of the platform itself but separate mini-applications, our task was to enable embedding them in the platform and collecting the data from those mini-apps on the client’s servers.
As the platform’s purpose was to gather sensitive medical data, it had to be fortified with proper security settings – aligned with GDPR compliance.
The client came to us equipped with a list of features that should have been built to enable the platform’s functioning. It was supposed to be a basic version, showing how the platform can help diagnose mental illnesses. At this point, the main goal was to build an MVP to present it to the stakeholders, including potential clients and investors.
When the client approached us for the first time, the first iteration was to be completed within only 4-6 weeks. They had a precise plan and list of goals crucial to them at this stage. They asked us to build solid foundations for all the features they needed (instead of building them entirely, one after another) so their in-house team could continue the work once our project ended.
As the platform was supposed to grow with time, it was crucial to the client that every component we built would be fully scalable. It also needed to be AI-ready, as its functionality was based on AI and machine learning.
As per the client’s request, and as it was crucial from the AI perspective, we’ve built the features to allow the user output to be sent by the media server and saved in storage.
Before the client reached out to us, they worked with an external developer who built the embedded mini-apps for them. We polished and updated them, ensuring their flawless operation.
Apart from the development support, the client was looking for a tech partner to widen the capacity and skills of their in-house front-end development team. Therefore, our engineers worked hand-in-hand with the client’s team, sharing their advice and know-how.
One of the steps of our cooperation was running a scoping workshop that helped the client map their requirements for the back-end and infrastructure of the platform. As a result, we equipped them with a complex plan, including a detailed workflow with technologies meant to be used at every step of the development process.
As a result of the first iteration, we have built a strong MVP of the platform that included the registration and log-in process, GDPR consent collection, user account, and patient dashboard and allowed the users to enter user tasks (mini-apps). Another important outcome of this iteration was the preparation of a proper data flow – enabling the collection of data recorded while users were interacting with the mini-apps. The MVP we built helped the client in closing a successful fundraising round.
In the second iteration, we’ve implemented a generic form builder that allows the creation of various types of questionnaires or surveys. We equipped the builder with several options and elements, such as the possibility of arranging polls in pages, a progress bar, open/closed/multiple-choice fields, a slider, and conditional field visibility. This feature became one of the user tasks, where the result – same as in the case of other mini-apps – was collecting a set of answers, helping in the diagnostic process.
In the third iteration we helped the client solve problems with the user tasks (mini-apps). As a result, they worked as they were supposed to, allowing significantly improved user interaction and the proper collection of data. It was crucial, as the tasks are the core element of the platform, allowing the system to collect the data necessary for the patients’ assessment.
The fourth iteration helped the client during the industry fair and allowed them to showcase one of the mini-apps (a mini-game) to the broader audience, explain the value of the platform, and prepare statistics from the data collected among people who played the game during the fair.
The platform was well received by the industry and has gained broad publicity – among others, it was featured in The Times.