How developing a mobile learning app with an ML-powered recommendation engine to provide each student with a relevant mentor empowered a US startup to set a foundation for a future educational ecosystem and increase students’ engagement by 43%.
The customer was looking for an IT partner – a technological and methodological expert who could help not only to create the product but also would assist in its market entry strategy. The idea was to develop an ecosystem which would fulfil the following functions:
Having a good idea, the customer, however, didn’t have any preliminary developments or a mere vision of how this idea can be embodied. The project was vague in terms of the interim goals, requirements, and technologies. Therefore, our team got a vast area of responsibility: we had to work out the vision and the business case of the product, deliver the design and, all in all, take part in the development as product owners.
As a part of a 3-day Pre-Discovery Workshop, our team dealt with the customer’s questions that had been piling up, plunged into their business context and aims, and, thus, together with the customer we prioritized the queries, built the plan and defined the scope of work for the next stages.
The app was supposed to be the first fragment of the future ecosystem and was expected to:
A line-up always matters but it becomes of vital importance on projects characterized by a high level of ambiguity. In order to ensure the optimal communication from the very beginning, we provided the customer with a wide range of team roles who are highly qualified experts specialized in solving particular classes of tasks: Business Analyst, UX/UI designers, Backend Tech Lead, Data Scientist, Solution Architect and Project Manager.
During the Discovery Phase we developed the solution which implied:
machine-learning module
chat functionality
In the result of close collaboration with the customer, *instinctools team has created a mobile application which provides access to all sorts of educational materials and allows to get consultations from people, whose career is a matter of interest for the users. Moreover, our company has gained excellent experience in developing recommendation systems that seek to predict the “rating” or “preference” a user would give to an item. Being welcomed in a variety of areas by giants such as Netflix, YouTube, Amazon etc., the benefits of these systems are now available for *instinctools clients.
Java 11
Docker 17.05 CE
Amazon Linux release 2
AWS EKS 1.14 (Kubernetes)
AWS ECR
AWS RDS (PostgreSQL 11.5)
AWS Route 53
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AWS Pinpoint
AWS Cognito
AWS SES
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Ansible
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SonarQube
Kubectl 1.147
Helm 2.14.3
Python
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