SHORT Case StudY

Eliminated all data integrity issues in a Shiny app driving medical research decisions

Appsilon redesigned the interface using custom Shiny components to improve usability.
BioPharma
A Fortune 100 leader in pharmaceuticals and biotechnology
Technologies used:
R
Python
R/Shiny

What business problem
did we face?

A key internal analytics tool for planning medical research had become hard to use. The interface was outdated, filters didn’t work, and the data was unreliable. This made it hard for teams to trust the tool or use it for key decisions.

The solution we
proposed

Appsilon redesigned the interface using custom Shiny components to improve usability. 

We rebuilt the data pipeline in Python to fix logic issues and boost data quality. The backend was also tuned for better performance.

We added two new data sources, a KPI dashboards, and refactored the codebase for long-term scalability and maintainability.

The impact of our solution
and its ROI

- Resolved 100% of data handling issues, restoring trust in analytics

- Improved UI/UX led to increased stakeholder engagement and adoption

Testimonial

“We went from struggling with unreliable data to having a trusted, scalable tool that now supports better decisions across teams.”

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