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Case studies

Three pieces of work, told as the problem that came in and what was built to answer it.

Research and technology implementation

The challenge

Members of a compensation fund and their families did not know about many of the benefits and services available to them. Communicating the mental-health support available during the pandemic mattered particularly, as did removing the need to travel for services that had never been offered online.

The solution

We implemented current Flutter technologies on a reactive, elastic and resilient architecture, with non-relational databases, microservices and agile working methods.

The new technology and the move to a disruptive, agile framework changed how the organization approached projects. The shift in the governance model was significant from both a technical and a working-culture point of view.

Centralisation of data for a pension fund company

The challenge

Make customer service more efficient by centralising the information it depends on: a single repository to check the status of cesantías — a savings fund people can draw on when a contract ends — along with voluntary and mandatory pensions, while improving the user experience.

The solution

We built a solution on a reactive architecture and migrated data from multiple sources into a non-relational database. We ran the migration iteratively and incrementally, with a rigorous quality and measurement process.

It became the centralised, approved source of information for the company: faster customer service, up-to-date data, extracts and certificates issued more quickly, a unified view of the information, and lower operating costs.

Identification of people for digital banking

The challenge

Guarantee that the right person is the one accessing the company’s digital channels, to keep the platform reliable and prevent fraud — for example, when someone requests a loan online.

The solution

We built an application around a prediction model using computer vision, which set a random challenge for the person requesting the loan.

That let us confirm the person was live and present, and prevent fraud through the model’s prediction and applied learning.

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