Frequently Asked Questions

Edge Semantics is designed for building owners, facility management teams, building service providers, engineers and systems integrators. It helps organisations responsible for building performance automate technical analysis, identify faults earlier, prioritise maintenance and improve operational efficiency.

Building data consists of raw information generated by equipment and systems, such as temperatures, energy consumption and operating conditions. Building intelligence adds context and analysis to that information, helping teams understand what is happening, identify potential problems and determine where action is required.

Edge Semantics operates through four key processes: modelling, analysing, visualising and predicting. The platform creates a structured digital representation of building systems, continuously analyses operational data using machine learning, presents insights through dynamic dashboards and identifies developing performance issues before they lead to equipment failures.

Building intelligence is the process of converting operational data from building systems into meaningful insights that support better decision-making. It uses technologies such as semantic modelling, automated analytics and machine learning to identify equipment faults, monitor performance and help facility teams manage buildings more efficiently.

Edge Semantics is a building intelligence platform that transforms complex operational data into actionable insights. By connecting building systems and equipment through a unified semantic model, the software automatically identifies faults, anomalies and performance issues, helping building owners and service providers improve operational efficiency and asset reliability.

Yes. Edge Semantics continuously analyses building operational data to identify abnormal performance, faults and signs of equipment degradation. These insights help technical teams investigate developing issues and intervene before they escalate into more significant equipment failures. Early detection depends on the availability and quality of relevant operational data.

Preventative maintenance follows predefined schedules to inspect or service equipment, regardless of its current condition. Predictive maintenance uses ongoing operational analysis to identify signs of degradation and determine when attention may be required. This supports more targeted maintenance decisions and can reduce unnecessary inspections and unexpected failures.

Predictive maintenance uses operational data and analytics to identify potential equipment problems before they result in failure. Rather than relying solely on scheduled inspections or responding after breakdowns, predictive maintenance helps facility teams plan interventions based on the actual condition and performance of building assets.

Edge Semantics uses machine learning and automated analytics to identify patterns, anomalies and signs of degradation in building operational data. By analysing information within a structured semantic model, the platform provides context around building systems and equipment, helping engineers understand where faults may be developing.

Automated fault detection uses software to continuously analyse building operational data and identify abnormal equipment behaviour, system faults and performance degradation. Unlike manual inspections, automated fault detection can monitor large volumes of data continuously, helping technical teams identify emerging problems and respond earlier.

Edge Semantics uses industry-standard semantic frameworks, including Project Haystack and Brick Schema, to organise building systems and operational data within a unified model. These standards help establish consistent relationships between equipment and data points, providing a flexible foundation for automated analytics, visualisation and interoperability.

Brick Schema is an open-source ontology for describing building assets, systems, equipment and their relationships. It provides a consistent framework for structuring building information, enabling applications to better understand how operational data relates to the physical building environment.

Project Haystack is an open-source initiative that standardises how data from buildings, equipment and IoT systems is described using semantic tags and relationships. It helps different systems interpret operational information consistently, making building data easier to organise, analyse and exchange.

A semantic building model is a structured digital representation of a building's systems, equipment, data points and their relationships. It provides context that allows software to understand how individual components relate to one another, supporting automated analytics, fault detection and operational visualisation.

Semantic modelling is a method of organising building data by defining the meaning of individual data points and the relationships between systems, equipment and spaces. It transforms disconnected operational information into a structured model that software can interpret, analyse and use to generate meaningful building intelligence.

Edge Semantics helps facility teams identify equipment faults and operational issues earlier, supporting more reliable building performance. By detecting developing problems before they result in significant disruption, building teams can take proactive action to maintain more consistent conditions for occupants. 

Edge Semantics helps facility teams spot underperforming systems early, ensuring occupant comfort is never compromised. By tracking everyday operating conditions, the platform catches minor efficiency losses before they cause noticeable comfort issues or unexpected disruptions. This allows teams to fix issues proactively, keeping the indoor environment perfectly stable, reliable, and comfortable for everyone inside. 

Building intelligence can support energy efficiency by providing greater visibility into operational performance and identifying abnormal equipment behaviour. These insights help technical teams investigate inefficient operation and determine where adjustments or maintenance may improve performance. Actual energy savings depend on the building, its systems and the actions taken.

Building intelligence can support longer equipment lifespans by identifying abnormal operating conditions and signs of degradation earlier. Timely maintenance and corrective action can reduce prolonged equipment stress and help assets operate closer to their intended performance conditions.

Edge Semantics continuously monitors operational data to identify emerging faults and equipment degradation. By providing earlier visibility into potential problems, the platform helps facility teams plan maintenance interventions before issues escalate into unexpected failures or emergency repairs.

Building analytics improves operational efficiency by automatically identifying faults, abnormal performance and equipment degradation. This reduces the need for manual data investigation and allows facility teams to prioritise maintenance, allocate resources more effectively and focus on resolving issues that affect building performance.

Building owners can improve cost efficiency by reducing repetitive manual investigations, identifying faults earlier and directing maintenance resources towards equipment that requires attention. Edge Semantics supports this approach through automated building analytics, helping teams make better-informed operational decisions without relying solely on additional labour.

Edge Semantics can help service providers improve their capacity to manage multiple buildings by reducing time spent manually interrogating building systems. Automated analysis enables engineers to focus their expertise on identified issues, supporting more efficient service delivery across their building portfolios.

Edge Semantics helps building service providers automate labour-intensive diagnostic activities. By continuously analysing operational data and identifying faults and anomalies, the platform gives engineers greater context before investigating issues, allowing them to spend more time on rectification, optimisation and delivering value to clients.

Edge Semantics helps facility managers understand building performance without manually reviewing large volumes of operational data. The platform automatically identifies issues and presents information through dashboards and visualisations, making it easier to prioritise maintenance, coordinate technical resources and manage building operations proactively.

Edge Semantics gives building owners and facility management teams greater visibility into building performance, equipment condition and emerging faults. Automated analytics helps teams identify problems earlier, reduce manual investigation, improve maintenance planning and make better use of existing operational resources.

You can request a demonstration of the Edge Semantics platform to explore how semantic building models, automated analytics and dynamic visualisations can support your building operations. A demonstration provides an opportunity to discuss your existing systems, operational challenges and potential applications for building intelligence.

Open standards such as Project Haystack and Brick Schema provide a consistent way to describe building equipment, data points and their relationships. This improves interoperability, reduces dependence on proprietary data structures and creates a more flexible foundation for analytics and future building technology applications.

Edge Semantics is designed to work with operational data generated by building systems, including HVAC, lighting, energy and other connected equipment. The platform uses semantic modelling to establish relationships between data points and equipment, supporting automated analytics and performance visualisation. Available analysis depends on connected systems and data quality.

Edge Semantics is positioned as an intelligence layer that brings together and analyses building operational data, rather than as a direct replacement for an existing Building Management System. It is designed to provide additional insights into equipment performance, faults and degradation. The exact relationship with an existing BMS should be assessed for each deployment. 

Edge Semantics acts as an intelligence layer that utilises the data from your existing Building Management System (BMS) to enhance occupant comfort. Rather than replacing your current system, it adds a new layer of analysis to spot underperforming systems early, catching minor performance drops before they cause noticeable comfort issues or unexpected disruptions. This extra visibility allows teams to proactively optimise performance and maintain a stable, comfortable environment for everyone inside.

Edge Semantics is designed to bring data from disparate building systems and equipment into a unified semantic model. By applying consistent metadata and relationships, the platform creates a structured foundation for analysing and visualising information across different operational systems. Specific integration requirements depend on the systems involved.