Monitoring infrastructure during construction is not a single task. It is a sequence of decisions, and each one depends on the quality of the last.
Most tools treat data collection, analysis, visualisation, alerting and reporting as separate steps. Sensly.io is designed differently. Sensly is built as a connected system that improves how monitoring work flows from data capture to reporting, reducing rework, preserving context, and helping engineers act with more confidence.
Convert raw time-series data into interpretable outputs using structured transformations and calculation logic. Sensly enables consistent interpretation across monitoring programs.
Raw sensor data, in its original form, provides limited value. Time-series readings viewed in tables or simple charts do not inherently communicate engineering meaning, particularly across large datasets or multiple sensor types.
Insight only emerges once data has been transformed, aggregated and interpreted using domain-specific logic. In many cases, this work is performed outside the monitoring platform, introducing fragmentation, version control issues, and inconsistencies in how data is interpreted across teams and projects.
Sensly enables engineering teams to define and apply transformation logic directly within the platform. Raw sensor inputs can be processed in real time to generate calculated parameters that reflect meaningful engineering metrics.
These calculated values become first-class data within the system. They can be visualised, used to trigger alerts, and included in reporting workflows, ensuring that interpretation is consistent, repeatable, and embedded within the monitoring process.
Custom analytics developed by integration partners.
Sensly provides a structured environment for transforming raw sensor data into consistent, interpretable outputs that support engineering decision-making.
Calculated parameters allow teams to define how raw sensor data is transformed into engineering values. Using JavaScript-based functions, engineers can apply project-specific logic directly within the platform, embedding their own methodologies into the data workflow.
This enables the generation of derived metrics such as displacement, rate of change, or other condition indicators that are not directly measured by the sensor itself. By formalising these calculations, teams ensure that interpretation is applied consistently across all sensors and datasets.
The calculation framework also supports calibration factors and other static variables that can be assigned at an individual sensor level and referenced within calculations. This is particularly useful for applying factory-supplied calibration constants unique to each instrument, as well as offsets, baseline readings, and other sensor-specific adjustments.
These values can also be changed with effective dates, allowing instruments to be rebaselined over time or known step changes in readings to be corrected without losing historical traceability. This provides a controlled way to manage sensor behaviour throughout the life of a project.
Calculated parameters operate in real time, meaning outputs are continuously updated as new data is received. This allows transformed data to be treated the same as raw inputs within the platform, without the need for external processing.
Sensly processes both raw and calculated data continuously as it is received. This ensures that transformations are applied immediately, without delays or the need for batch processing.
As a result, calculated outputs are always current and available for visualisation, alerting and reporting. This supports time-sensitive monitoring scenarios where delays in interpretation could impact decision-making.
The real-time processing approach also reduces reliance on external tools, keeping data workflows contained within a single, controlled environment.
Transformed data feeds directly into visualisation, alerting and reporting workflows, removing the need to export and reinterpret data in external systems.
By embedding transformation logic within the platform, Sensly ensures that the same calculation methods are applied consistently across sensors, teams and projects.
Transformed data can be visualised using chart types designed for geotechnical and structural monitoring. These provide context that standard charts do not, helping engineers interpret behaviour more effectively.
Sensly provides a structured environment for transforming raw sensor data into consistent, interpretable outputs that support engineering decision-making.
