A local-first classroom product family that brings environmental sensing, on-device intelligence and permission-aware dashboards into one understandable school system.
One local-first idea. Two classroom-ready configurations.
Both models are fully local devices designed to sense, process, store and explain conditions continuously. Internet access adds the authorised portal; it is not required for classroom monitoring, history or local response.
Compact classroom intelligence
EXPO Mini
Starting at₹14,000per classroom unit
ProcessingESP32 local controller
Local by designOn-device storage with a dedicated SD card module
Environmental sensingPMS5003 particulate sensing for PM1.0, PM2.5 and PM10
ClimateSHT40 high-accuracy temperature and humidity sensing
Air-quality channelsDedicated NDIR CO₂, ENS160 TVOC/eCO₂ and an electrochemical CO channel
Gas coverageFour selected MQ channels: MQ-2, MQ-3, MQ-5 and MQ-8
Local operationCreates its own local setup hotspot and continues monitoring without internet
Real-time local responseContinuously updates the local dashboard and can trigger on-device outputs within milliseconds after the safety engine validates a DANGER state
School portal₹99per unit / month
AI-assisted dashboards for authorised parents, staff and administrators, with role-appropriate classroom access.
Product roadmap notice: these are planned commercial configurations and indicative starting prices. Final sensor models, enclosure, certifications and pricing may change after pilot validation. ENS160 provides TVOC and estimated CO₂ context; the dedicated NDIR channel is used for true CO₂ measurement. Sensor detection time still depends on each sensing element and the configured confirmation window; millisecond response refers to local actuation after a danger state has been validated.
Product presentation
Small classroom hardware. A complete operational picture.
The physical device is only one part of CLASSPERE. The useful product is the relationship between sensing, local interpretation, visible feedback and a history the school can review.
Local-first Baseline-aware History enabled
CLASSPERE AI MONITORING
CLASSROOM 8ASAFEAir conditions stable
CO2TEMPMQ ARRAY
01 / Product anatomy
Environmental sensing, with honest labels.
Temperature and humidity come from the DHT22. The MQ channels are used for calibrated, trend-based estimates and are never presented as laboratory-grade gas analysis.
TemperatureDHT22
Room temperature for context and comfort.
HumidityDHT22
Relative humidity to help interpret the room.
MQ-2Smoke / LPG
Estimated gas trend from the calibrated response.
MQ-3Alcohol vapour
Estimated vapour trend for prototype monitoring.
MQ-4Methane
Estimated methane-related response trend.
MQ-5LPG / natural gas
Estimated LPG and natural-gas response trend.
MQ-7Carbon monoxide
Estimated CO response trend, not a certified alarm.
MQ-8Hydrogen
Estimated hydrogen-related response trend.
02 / Local intelligence
From sensor packet to useful decision.
The local path keeps the classroom responsive and keeps the important processing visible.
SensorsEnvironment
ArduinoAcquisition
USB serialTransfer
Raspberry PiLocal engine
Calibration + baselineInterpretation
SQLite historyEvidence
Dashboard + alertsResponse
03 / Lifecycle
From boot to useful reading.
01Power on
02Sensor warm-up
03Calibration
04Baseline establishment
05Monitoring
06Classification
07History
04 / Specification
The details that matter during a pilot.
Processing architecture
Arduino acquisition plus Raspberry Pi local engine
Sensor packet interval
One complete packet every second in the current prototype
Monitored channels
DHT22 temperature and humidity plus MQ-2, MQ-3, MQ-4, MQ-5, MQ-7 and MQ-8
Communication
USB serial from Arduino to Raspberry Pi
Local database
SQLite history on the Raspberry Pi
Connectivity model
Local monitoring continues; cloud portal is an optional authorised view
Safety states
SAFE, ELEVATED, WARNING and DANGER
Cloud role
Authenticated access, school views, device heartbeat and historical review
