Pathway
The Building AI Pathway
A structured, evidence-led process that takes buildings from initial assessment through to AI-augmented performance, with clear milestones and measurable outcomes at each stage.
Phase 16–12 months
Health & Uplift
Audit the building, deploy independent sensors and establish baseline data quality.
| Service | Description |
|---|---|
| Schematic Review | Review of mechanical, electrical, metering, and controls schematics to understand system design and key operational dependencies. |
| O&M and Design Information Review | Review of available O&M manuals, asset schedules, control descriptions, commissioning information, and design intent documentation. |
| Building Operation Review | Assessment of how the building is currently operating, including plant operation, control strategies, schedules, setpoints, comfort conditions, and energy use. |
| BMS Review | Review of BMS structure, available points, alarms, trends, naming conventions, control logic, sensor coverage, and integration options. |
| Metering Review | Review of main meters, submeters, asset-level meters, data quality, missing data, and suitability for future performance reporting. |
| Independent Sensor Review | Deployment and review of temporary wireless sensors to provide an independent assessment of building conditions outside the BMS. |
| Data Quality Review | Assessment of data completeness, consistency, reliability, naming quality, sensor accuracy, and suitability for baselining. |
| Operational Gap Analysis | Identification of faults, missing data, poor control behaviour, insufficient sensing, inefficient operation, and uplift requirements. |
| Uplift Recommendations | Practical recommendations for improving readiness for baseline development, optimisation, and AI augmentation. |
Phase 212 months
Building Baseline
Continuously monitor to build a defensible model of how the building actually performs.
Foundation for long-term energy savings
| Service | Description |
|---|---|
| Baseline Methodology Review | Definition of the baseline approach, including data sources, reporting periods, performance metrics, weather adjustment, occupancy assumptions, and measurement boundaries. |
| Building Performance Review | Review of how the building performs across seasons, including energy use, comfort conditions, plant response, system interaction, and operational consistency. |
| Building Behaviour Modelling | Development of a working model of how the building responds to weather, occupancy, schedules, internal loads, and control strategies. |
| BMS Data Review | Ongoing review of BMS data to understand system behaviour, control performance, alarms, faults, trends, and operating patterns. |
| Metering and Energy Review | Review of metered energy consumption, peak demand, submeter relationships, abnormal loads, and asset-level energy behaviour. |
| Environmental Performance Review | Review of temperature, humidity, CO2, comfort conditions, and environmental stability across occupied and unoccupied periods. |
| AI Learning Period | Use of collected data to allow AI models to learn normal building behaviour, system response, inefficiencies, and operational patterns. |
| Automated Baseline Reporting | Production of automated reports showing energy use, comfort, system performance, anomalies, data quality, and emerging opportunities. |
| Investment Decision Support | Evidence-led support for future capex decisions including solar PV, batteries, controls upgrades, plant replacement, metering improvements, and optimisation priorities. |
| Baseline Sign-Off | Agreement of the baseline position to act as the reference point for future operational changes, savings verification, and investment appraisal. |
Phase 312 months
Energy Saving
Apply proven static optimisation — schedules, setpoints, sequencing — with measurable, audited savings.
Expected savings: 30% to 60% across Phase 1 to Phase 3
| Service | Description |
|---|---|
| Static Optimisation Strategy | Development of a controlled energy-saving strategy based on baseline findings, building behaviour, system limitations, and operational priorities. |
| Control Strategy Review | Review and refinement of heating, cooling, ventilation, lighting, hot water, plant sequencing, schedules, setpoints, and demand management strategies. |
| Schedule Optimisation | Adjustment of operating schedules to reduce unnecessary runtime while maintaining comfort, compliance, and operational requirements. |
| Setpoint Review | Review and optimisation of temperature, ventilation, pressure, flow, and other relevant setpoints to reduce energy waste without compromising performance. |
| Plant Sequencing Review | Review of how plant is enabled, staged, sequenced, and controlled to identify inefficient operation and improve system performance. |
| Energy Saving Implementation Support | Support for implementing agreed operational changes through the incumbent BMS, facilities team, or controls contractor. |
| Savings Measurement Review | Comparison of post-optimisation performance against the agreed baseline to measure energy reduction, cost savings, and comfort impact. |
| Comfort and Compliance Review | Ongoing assessment to ensure energy-saving changes do not negatively affect comfort, environmental conditions, statutory requirements, or service levels. |
| Automated Optimisation Reporting | Regular reporting on savings achieved, comfort impact, exceptions, abnormal behaviour, and further optimisation opportunities. |
| AI Readiness Review | Assessment of whether the building is sufficiently stable, corrected, and data-rich to proceed to AI augmentation. |
Phase 412 months & ongoing
AI Augmentation
Predictive, adaptive AI on top of a stable BMS. Continuous learning, predictive maintenance, autonomous optimisation.
| Service | Description |
|---|---|
| AI Augmentation Strategy | Development of the AI operating strategy, including use cases, control boundaries, risk controls, reporting requirements, and integration approach. |
| Predictive Control Review | Review of opportunities for AI to support predictive control using weather, occupancy, thermal response, plant behaviour, comfort data, and historical performance. |
| AI Model Deployment Support | Support for deploying AI models that learn building behaviour, identify optimisation opportunities, and recommend or apply improved control strategies. |
| BMS Integration Review | Review of how the AI layer interfaces with the incumbent BMS while preserving local control, resilience, safety, and operational continuity. |
| Continuous Optimisation Review | Ongoing assessment of how AI-led strategies improve energy use, comfort, plant operation, demand management, and system efficiency over time. |
| Anomaly Detection Review | Use of AI and automated analytics to identify abnormal behaviour, sensor drift, plant faults, excessive runtime, and degradation in performance. |
| Savings Verification Review | Measurement of AI-led savings against the agreed baseline and Phase 2 optimised position to verify additional benefit. |
| Operational Overwatch | Continuous monitoring of the building, with alerts, reports, recommendations, and escalation of issues requiring operational attention. |
| Investment Performance Review | Assessment of whether installed assets, control changes, or capex investments are performing as expected against the baseline and business case. |
| Long-Term Performance Reporting | Ongoing reporting on energy savings, comfort, compliance, system performance, anomalies, and future improvement opportunities. |
Optional Entry Tier
Unvalidated Lite — Rapid Insight
Unvalidated Lite is a rapid connection service that presents the building data as it currently exists, without validation, correction, or interpretation. It connects to available BMS, utility, and space-condition data points and displays them through the Future Decisions platform to provide immediate visibility of what is already present in the building.
Use Cases
Rapid initial visibility
Early-stage engagement
Demonstration of platform value
Portfolio visibility exercises
Buildings with limited documentation
Pre-validation discovery before Phase 1
Note
This tier is intended for early awareness, demonstration, and discovery only. It does not verify whether meters are correct, sensors are accurate, points are mapped properly, or data is suitable for compliance, optimisation, or investment decisions.
Unvalidated Lite provides visibility only. It does not provide validated insights, compliance assurance, optimisation recommendations, or investment-grade analysis. Its just to get you in the game and the data to all stakeholders.
Ready to Begin?
Start with a Building Health Check to understand how your building truly performs and what steps are needed to achieve measurable, verifiable savings.
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