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OpenBlue deploys cutting-edge Artificial Intelligence to drive high-value strategic objectives, including data-driven forecasting, autonomous systems control, and performance optimization.
- Making AI accessible: Helping customers adopt AI without needing to know where to start or what questions to ask.
- Using generative AI for insights: Providing clear answers to pre-defined questions about facility performance, energy usage, and operational efficiency.
- Energy benchmarking: Comparing current energy consumption against traditional baselines to identify improvement opportunities.
- Actionable recommendations: Suggesting ways to reduce energy expenditure and improve sustainability footprints in buildings.
- Enhancing decision-making: Delivering specific, data-driven insights that empower customers to optimize their facilities effectively.
Benefits of AI
Actionable observations and recommendations
Concise information enables clear understanding of building issues and recommends ways to minimize unexpected issues.
Data-driven decision making
Leverage historical and forecast data to make informed decisions on areas such as equipment performance, energy usage or space utilization.
Autonomous optimization
Closed-loop approach automatically responds to changing needs from occupants and environmental conditions.
Avoid unnecessary disruptions
Factor-in multiple datasets to run simulations and what-if scenarios without disrupting your building operations.
Safer, healthier spaces
Combine internal building data with external factors to improve occupant experience and operational efficiency.
Reduction in downtime
Analyze equipment performance data with operational factors to proactively identify issues before they materialize.
Building Intelligence
Turn network data into a business advantage.
Trends and Forecasting
OpenBlue uses time series prediction models across multiple data domains to enable forecasting and budget planning.
Energy Consumption: Ability to plan operations and budgets with load based forecasts.
Facility Occupancy: Predictive models accurate predict occupancy needs allowing for more efficient space usage.
Water Usage: Efficient water management usage reduce energy use and contributes to conservation efforts.
Agentic AI
Our AI agents provides quick access to data with intuitive interaction and accurate responses.
Visitor Assistant: Natural voice and chat across web, mobile, and kiosks for enhanced visitor experience with specialized AI agents.
Space Insights Agent: Real-time conversational responses for instant, accurate spatial data insights for users of all technical levels.
Space Utilization Agent: AI-powered storyteller that interprets space utilization data and explains trends and insights to your team.
Observations & Recommendations
Our Gen AI engine provides automated, intuitive recommendations that empower fault, energy, emissions, and space optimization.
Compliance Monitoring: Assesses forecasted emissions and energy use against regulatory limits or targets.
Insights: Recommends energy, emissions and space utilization measures with the highest potential impact.
Fault Detection & Maintenance: Recommendations that summarize proactive actions to be taken and potential energy savings.
Autonomous Equipment Operations
Autonomous controls optimizes equipment operations and ensures critical setpoints are in range.
Central Plant Optimization: Autonomous and optimized equipment setpoint commands.
Fault based Closed Loop Control: Adjusts setpoints based on your defined constraints and conditions to mitigate high impact results.
Clean Air Optimization: Continual assessment of clean-air metrics and physics-based model simulation for airflow and energy prediction.
Setup & Deployment
OpenBlue AI powered ROBOT alleviates manual processes required, increases accuracy and decreases deployment time.
Data Point Ingestion: Reduces onboarding time yielding faster realization of value.
Automated Asset Classification: Predicts point and equipment classes that ensures consistency and interoperability across downstream systems.
OpenBlue AI Commitment
Our OpenBlue AI team operates within established guide rails to ensure AI is used for the betterment of our customers.
Our AI mission is: To improve the lives, working environment and operations of our customers by integrating AI tools that manage time consuming, data heavy, and routine tasks enabling our customers to focus on their most critical items.
Data Privacy
Data Privacy is embedded into the development of our openblue products. Data used for AI applications is kept within our Microsoft Azure cloud environment and subject to the same data privacy agreement. Data is not shared with LLM platforms or other external providers.
Humans in the loop
No matter how advanced our AI based solutions evolve, human judgement remains integral in the supervision of AI outputs, validation of AI suggestions, improvement of AI models and the ability to override AI based information when needed.
Outcome-driven
Our AI approach puts practical results and value above technology hype, focusing efforts on solving real customer problems and measuring success by the positive impact it achieves.
Zero trust cybersecurity
In addition to making security a critical part of our product design process using a holistic, structured approach, our own OpenBlue Airwall cyber security software is embedded into our platform protecting you from invisible threats with an identity base zero trust platform that secures any type of device.
Guardrails
AI in OpenBlue embeds comprehensive guardrails that ensure safe and ethical operations within its intended boundaries. Preventing undesirable or harmful behaviors from our solutions is of upmost importance.
Fit for every situation
Our extensive AI toolkit allows us to select the most effective approach for every unique challenge. This flexibility ensures that every solution is tailored to your specific goals, driving efficiency and sustainability at scale.
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OpenBlue AI FAQs
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What confidentiality safeguards are in place?
Data used for generative AI applications is kept within our Microsoft Azure cloud environment and subject to the same data privacy agreement. For machine-learning applications, customer datasets may be used to inform high-level algorithm design or default hyperparameters. However, separate models are trained for each customer, asset, etc., which keeps them fully confidential.
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Does your solution have an AI risk model when developing or implementing your solution's AI model?
Yes. All AI use cases go through a standard assessment process, which includes risk evaluation.
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Can your solution's AI features be disabled by user?
Yes, if desired, all AI features can be disabled by the user.
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Does your solution support business rules to protect sensitive data from being ingested by the AI model?
As one of our key AI principles, all customer data used by the models comes customer buildings via the OpenBlue platform. No personal or other sensitive information is used.
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Are your AI developer's policies, processes, procedures, and practices across the organization related to the mapping, measuring, and managing of AI risks conspicuously posted, unambiguous, and implemented effectively?
All AI use cases go through a standard assessment process, which touches on those features .
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Will any data be shared with external providers, including LLM platforms?
Data is not shared with LLM platforms or other external providers. Data that is processed to generate the insights, stays within the same cloud environment as the rest of the OpenBlue software suite and meets all security and compliance policies.
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Are there safeguards in place to prevent external LLM providers from using our data for model retraining?
LLM providers (such as OpenAI run on top of Microsoft Azure) do not use customer data for model training, as we use this within the OpenBlue cloud environment. With OpenBlue, the models we use are pre-trained, we host them in Azure and not accessible to outside xxx… OpenBlue AI is compliant with standards like GDPR, HIPAA, and ISO/IEC.
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If sensitive data is introduced to your solution's AI model, can the data be removed from the AI model by request?
ML models do not have access to sensitive data. We do not do any LLM fine-tuning, so no data is persisted in LLMs.
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Do you plan for and mitigate supply-chain risk related to your AI features?
Yes. Source code is automatically scanned for usable of vulnerable packages.
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Have you limited access to your ML training data to only staff with an explicit business need?
Yes, only those employees with an essential need has access to training data..
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Is there any type of performance monitoring for AI-based capabilities?
Accuracy of timeseries prediction models is monitored regularly, and models are re-trained if accuracy falls below a given threshold. In the future, customers will also be able to provide direct feedback on Generative AI outputs, which will be used for further monitoring purposes.
OpenBlue insights
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