AI Ready Data Demonstration - Indoor Environmental Quality
This demo visualizes three environmental metrics from the Taika Meeting Room for a workweek in January 2026. Each metric gets its own heatmap strip with a continuous color gradient — revealing patterns that a single composite score would hide. All data is loaded directly from the Haltian IoT Data API.
What You Can Build
Our platform delivers structured, AI-ready data — you bring your preferred tools. Feed it to ChatGPT, Power BI, Python, or any analytics platform and build exactly what your facility needs. This page includes an AI prompt you can copy — jump to prompt →
This demo uses environmental data from a Thingsee AIR sensor on the Haltian IoT platform. Learn how to access this data programmatically via the Data API.
The visualization below was built by AI in minutes using Haltian IoT data — it is not a Haltian product.
🌡️ Indoor Environment — Metric Strips
Taika Meeting Room • Week of Jan 20–24, 2026 • Office Hours 8:00–16:00 EET
About This Demo
This visualization shows three separate environmental metrics from a single Thingsee AIR sensor, each with its own color scale and patterns. Unlike a composite score that flattens everything into one number, the metric strips let you:
- See each metric’s story independently — TVOC might be stable while CO₂ fluctuates
- Spot time patterns — “CO₂ spikes every afternoon at 14:00” vs “temperature rises after lunch”
- Compare across days — is Wednesday consistently worse than Monday?
- Identify the root cause — not just “air quality was poor” but specifically “humidity was too low”
Metrics Shown
| Metric | What It Tells You | Actionable Insight |
|---|---|---|
| CO₂ | Ventilation effectiveness | “Open windows” or “check HVAC schedule” |
| TVOC | Air purity (cleaning products, off-gassing) | “Increase fresh air intake” |
| Temperature | Thermal comfort | “Adjust thermostat” or “check sun exposure” |
Color Scales
Each metric uses a continuous gradient mapped to its own optimal range:
- Green = optimal range for that metric
- Yellow/Orange = outside comfort zone, needs attention
- Blue (temperature only) = too cold
Data Source
- Device: Thingsee AIR (Meeting Room Taika, 1st Floor, Oulu Headquarter)
- Measurement Types:
measurementCO2,measurementTVOC,measurementAmbientTemperature - Time Period: January 20–24, 2026 (one workweek)
- Resolution: Hourly averages during office hours (8:00–16:00 EET)
All data processing happens in your browser using hyparquet — no server required.
Build This with AI
Want to recreate this demo or build your own variation? Copy this prompt to your AI coding assistant:
You are an indoor environment analytics expert. Build me a multi-metric environment dashboard.
Step 1: Read the Haltian IoT data documentation at https://developer.haltian.io/haltian-iot/apis/data-api/ to understand the data model.
Step 2: Study the implementation at https://developer.haltian.io/demos/ai-demonstration-indoor-environment/ including:
- The metric strip layout (3 separate heatmaps stacked vertically)
- The continuous color gradient approach (not bucketed scores)
- The insight cards highlighting peak/min values with timestamps
Step 3: Build me a multi-metric indoor environment dashboard using your preferred technology stack.
Key requirements:
- Load CO₂, TVOC, and temperature data from Parquet files
- Display each metric as its own strip heatmap with a continuous color gradient
- Show actual values in cells (not scores/letters)
- Each metric uses its own color scale appropriate to its range
- Include insight callouts: peak CO₂, avg temperature, avg TVOC
- Show min/max/avg stats per metric
- Hover reveals exact value + timestamp
- Thresholds: CO₂ (<800/1000/1500 ppm), TVOC (<220/660/2200 ppb), Temp (21-23/20-24/18-25 °C)