Nick Tune
CEO/Co-founder
O
Optimise
The startup develops AI-powered digital twin technology that creates virtual replicas of non-domestic buildings to analyze energy consumption patterns. This technology enables facility managers to identify efficiency improvements, leading to significant cost savings and reduced carbon emissions.
- Artificial Intelligence
- Data & Analytics
- Clean Technology
- Enterprise Software
- Internet of Things
- Software Only
Updated 21 months ago
Funding
Raised to date
$930KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Funding rounds are not available yet.
Founders
Product
Problem
Many non-domestic buildings lack comprehensive operational data, hindering efforts to reduce energy consumption and carbon emissions. The absence of building management systems (BMS) in a significant portion of these buildings, coupled with rising energy costs and stringent carbon emission reduction targets, creates a challenge for efficient energy management.
Solution
Optimise AI offers an AI-powered digital twin technology that creates virtual replicas of non-domestic buildings, enabling real-time energy performance analysis and optimization. The platform integrates data from various sources, including BMS, IoT sensors, and energy meters, to provide a unified view of a building's energy usage. By leveraging machine learning and semantic digital twin technology, Optimise AI delivers actionable insights for reducing energy costs and carbon emissions, offering potential savings of up to 40%. The solution includes two main products: Predict, which provides a portfolio-wide overview using minimal data, and Optimise, which creates granular, room-level digital twins for deeper insights and control.
Target Audience
The primary target audience includes estate asset managers, facility managers, and sustainability managers responsible for optimizing energy performance and reducing carbon emissions in non-domestic buildings such as train stations, airports, universities, and commercial properties.
Features
- Digital twin creation for non-domestic buildings, enabling virtual replicas for analysis
- Integration with existing BMS and IoT sensors for comprehensive data collection
- Real-time monitoring of energy consumption, carbon emissions, and costs
- AI-powered analysis and recommendations for energy saving measures
- Comparison against industry benchmarks, EPCs, and net-zero building standards
- Granular mapping of buildings down to room level for precise insights (Optimise)
- Dynamic building control, adjusting HVAC settings based on occupancy (Optimise)
- Automated investment planning scenarios with ROI calculations (Optimise)