Technology

One intelligence layer. Multiple levels of protection.

From a wireless sensor node to a portfolio-wide risk index — the architecture behind Environmental Sentinel™.

System Architecture

How data becomes intelligence.

Four stages turn a five-minute sensor reading into facility-specific risk intelligence.

  1. Sense

    Wireless, continuously powered nodes collect data every five minutes and stream it over a secure, cloud-connected platform.

    • Wireless sensor nodesTemperature, humidity, and surface-condition readings across the facility.

  2. Understand

    Raw readings are measured against each facility's own normal, not generic thresholds.

    • Facility-specific baselinesEvery facility gets its own reference point.
    • Dew-point & condensation-margin analysisThe margin between current conditions and condensation risk.
    • Historical trend analysisNormal variation separated from emerging risk.
    • Environmental risk indicesA single composite score for at-a-glance status.

  3. Predict

    Models watch for the conditions that tend to precede moisture and biological problems.

    • Predictive anomaly detectionAI-supported flags for conditions statistically associated with moisture and biological risk.
    • Environmental risk mappingWhere risk concentrates across a facility.

  4. Act

    Intelligence reaches the people who act on it, with robotics extending the reach over time.

    • Cloud-connected dashboardsRead-only views built for facility leaders, not just engineers.
    • Future robotic inspection & interventionCleanBot™ extends the architecture to physical follow-through.

The platform does not claim to directly identify mold species. Anomaly detection flags environmental conditions statistically associated with moisture and biological risk, for review by facility teams.

Research & Development

Developed alongside serious engineering partners.

Smart Surface Robotics is working with leading research and engineering organizations in areas such as autonomous robotics, multi-robot systems, adaptive adhesion, surface mobility, environmental sensing, machine learning, autonomous inspection, and biological-risk mitigation.

Research and development relationships include work with teams affiliated with organizations such as Carnegie Mellon University’s National Robotics Engineering Center, Georgia Tech research laboratories, Virginia Tech researchers, and engineering and manufacturing collaborators. These references describe research relationships, not formal institutional endorsements.

Curious how this maps to your facility?

We'll walk through the architecture in the context of your buildings, systems, and existing monitoring.

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