Crafting an updated Heat Map at Cortex
The floor temperature heatmap is a widely appreciated feature in the Cortex app, but it came with several key limitations:
Lack of actionable insight: While engineers could identify temperature anomalies, the heatmap didn’t provide context or next steps—forcing users into time-consuming investigations to determine the root cause.
No prioritization of issues: The view showed all data equally, making it hard to quickly identify outlier floors with true comfort-impacting issues, such as being too hot or too cold.
Missing operational context: There was no way to capture or share floor-specific conditions—like unoccupied areas or ongoing construction—which could justify temperature deviations. That information lived in engineers' heads, limiting collaboration and alert accuracy across teams.
Cortex Process
The Cortex design and engineering process began with identifying a clear user need. Through on-site visits and customer feedback calls, we learned that the floor temperature heatmap needed significant improvements.
I began by mapping the design landscape with detailed user flows and early Figma layout blocking. With a 6-week engineering cycle, design was front-loaded and generally defined through multiple iterations within the first 7–10 days.
Since the project spanned multiple areas of the app and introduced new data, I also defined notification logic and built the supporting information architecture to guide in-app messaging.
These early diagrams were key in aligning the project team and documenting decisions. Initial concepts were vetted internally and with select users, and their feedback informed the final refinements.
Preliminary diagramming
These example diagrams, sourced from the project’s Figma and Miro files, demonstrate the level of detail applied across different areas of the product design process.
One of several logic flows created to illustrate how the data for temperature readings would be used in various UI elements.
The intent of all of this was to tie together how and why we were building various elements into the app. For example, knowing occupied or construction state of a given floor required collecting the data and then exposing the data through the UI to users.
Schemas and states were explored to validate design and colors, test with users. Finalized designs were used inform the engineering team.
All of this came together in the Figma file as a detailed, organized definition of the design. Engineering especially valued the clarity—each element was clearly documented, with guidance on usage, page-specific data, and component variants, making implementation more efficient.
Insights and alerting
As the heatmap evolved, we introduced a more actionable version on the insights dashboard—highlighting floors with consistent temperature deviations across defined time intervals. These surfaced issues gave engineers quicker visibility into patterns needing attention without digging through full-building data.
The design was intentionally scalable and context-sensitive. It only displayed alerts when floor temperatures were both out of range and relevant—factoring in user-supplied status tags like “unoccupied” or “under construction.” While these floors still appeared on the full heatmap for completeness, they were excluded from the insights dashboard to reduce noise and prioritize tenant comfort.
This example shows a fully populated view of problem floors with temperature trends. The most critical items—floors outside the expected temperature range for the day—are highlighted. In addition to visual indicators and links to the full Heat Map dashboard, users received SMS alerts when daily floor temperatures require attention.
The Temperature Heat Map Dashboard
Provides a time-based view of all building floors, with temperature readings monitored in defined intervals. Floors are color-coded to indicate when temperatures fall outside the intended range, allowing users to quickly identify issues and assess how long they’ve persisted. This enabled engineers to make timely decisions to maintain tenant comfort.
In addition to real-time insights, the heat map also revealed broader temperature trends across the building—supporting energy efficiency efforts over time.
Results
• Actionable insights: Engineers can now quickly identify not only when a temperature is out of range, but why—reducing time spent investigating and enabling faster, more confident decision-making.
• Prioritized issue detection: Outlier floors with real comfort-impacting problems are now clearly highlighted, helping users focus on what matters most and act efficiently.
• Shared operational context: Floor-specific conditions like construction or vacancy can be added and seen by the whole team, improving alert relevance, team alignment, and overall collaboration.
