Overview
FacilityBot Studio is an AI-powered app and dashboard builder built into FacilityBot. It enables custom applications and dashboards to be easily created simply by describing their requirements in natural language, without engineering support or manual dashboard configuration.
From “Here’s what we need…” to “Here’s the dashboard.” ⚡ Just describe what you need. FacilityBot Studio builds it.
In this case study, FacilityBot Studio was used to build a custom sensor analytics dashboard for a facilities management provider overseeing public food centres in Singapore. The dashboard combines two sensor types — people counters and ammonia (NH₃) sensors — installed in restrooms across multiple sites, giving the operations team a single view of occupancy and odour conditions to help plan cleaning schedules.
The Requirement
The team already had sensors deployed at scale — people counters and ammonia sensors across dozens of restroom locations — but no single dashboard to make sense of the readings.
Each sensor reported data on its own, so tracking which locations needed attention meant checking devices individually.
They needed one dashboard that could group readings by cluster and location, break people counts down by gender and accessibility category, track ammonia levels and environment conditions over time, and show whether every sensor was still online — all without engineering support to build or maintain it.
How It Was Built with FacilityBot Studio
Using FacilityBot Studio, the dashboard was built and then refined over several rounds simply by describing each requirement in natural language. Instead of manually wiring up new charts or tables, Studio was instructed to add, adjust, or remove sections while preserving the parts of the dashboard that already worked.
Here is an example of a prompt used:
“Build a sensor dashboard with an overview of total people in, people out, net flow, and average ammonia reading for the selected month. Add a People Counter tab grouped by cluster and location showing in/out counts by gender and accessibility category, an Ammonia tab showing average, max, and latest readings by location, an Environment tab tracking temperature and humidity trends, and a Device Health tab listing every sensor’s last-seen time, battery level, and online/offline status. Add CSV export to every table.”
As new needs came up, Studio was used the same way to keep iterating — separating live and offline device counts, removing unused columns, and adding filters by cluster, location, gender, and device type — each time by describing the change rather than editing the dashboard by hand.
The Result
The team now has one dashboard covering occupancy and air quality across every monitored restroom, with data grouped by cluster and location and broken down by gender and accessibility category. An Environment tab tracks temperature and humidity, and a Device Health tab shows battery levels and online/offline status for every sensor, so maintenance can be planned before a device drops off entirely.
Every table can be filtered by month and exported to CSV, so the team can review trends over time or share the data without compiling any of it manually.
AI Studio enables teams to quickly build and refine dashboards like this one, making changes through natural language instead of manual configuration or engineering support.