Pharma teams need a faster way to find meaning in large volumes of patient, clinician, and market conversations. Insight-D uses AI to surface topics and emerging trends, then links each finding back to its source text.
01 / Insight-D
Insight-D uses AI to identify key topics, monitor known issues, surface emerging trends, and connect findings back to source context. I partnered with a data analyst to shape how those signals could be explored in a clear, interactive dashboard.
02 / From questions to views
In discovery with the data analyst, I mapped the questions people needed to answer to the right visual and controls. The filters let users narrow the analysis by date, country, requester, product, and response status, then move from topic summaries into source language.
Select a region to add it to the dashboard filters.
Toggle a series to compare how topic volume shifts across the selected period.
Choose a category to focus the breakdown.
Choose a row to see the topic count.
Choose a topic to inspect its count.
Select a keyword to see a sample source excerpt.
Stability: Patients who have spent one and a half hours transporting a device in a special container from a healthcare facility to their home may be exceeding the safe temperature. How do you know?
03 / Visual exploration
The dashboard pairs spatial, temporal, and ranked views with source language. I explored how color and composition could help people distinguish topic groups without overwhelming the data.
Palette study
Switch palettes to compare the tone and contrast of the same chart composition. These are visual explorations for the case study.
04 / Sharing the value
I also shaped product communication for a broader audience. This presentation introduces Megaputer’s hybrid AI approach and frames the platform’s value around accuracy, simplicity, and scale.
05 / Product videos