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PassiveLogic

Autonomous building control powered by physics-informed AI

Platform for designing, deploying, and operating autonomous building systems using digital twins and generative AI to optimize energy efficiency and occupant comfort.

$125M+ (Series C: $74M in September 2025) raisedGrowth Stage
Founded 2016 · Aroundtown (major European real estate, M-DAX listed) and pilot customers across EU and North America
Website →← All Tools

Why This Tool Exists

The Problem

Buildings contribute 40% of global carbon emissions and operate inefficiently due to legacy control systems based on 1880s-1930s thermostat logic. Manual HVAC commissioning, system tuning, and maintenance require months of engineering work and result in suboptimal performance.

The Solution

Physics-informed digital twins combined with generative AI enable autonomous real-time building control at the edge. PassiveLogic's platform automatically optimizes energy efficiency, comfort, and maintenance through predictive control that simulates millions of futures per second.

How You Use It

Delivery Method
SaaSHardwareAPI
Integrations
Quantum API (custom integrations)BIM integration via digital twinsHardware: Hive controller, Sense Nano sensors
Project Phases
Pre-Design, Construction Administration, Operations/FM
Project Types
Commercial, Mixed-Use, Institutional, Industrial, Healthcare

Data Transparency

Exactly what this tool uses and how

Input
What it needs
Required:Building mechanical systems, Sensor data, Building envelope specifications
Optional:BIM models, Historical operational data, Occupancy patterns, Equipment specifications
Formats:IFC, Quantum digital twin format, Sensor data streams (real-time), LIDAR point clouds
Output
What you get
Format:Real-time autonomous control decisions and building management dashboard
Fields:Autonomous control signals to mechanical systems, Energy consumption optimization, Occupant comfort metrics, Equipment maintenance alerts, Predictive maintenance recommendations, Carbon reduction tracking, ESG/EPBD compliance reporting
Algorithm
How it works
Model:Physics-informed machine learning: Quantum digital twins fusing deep learning with physics-based simulation
Accuracy:Field deployments show 30% energy savings vs. conventional control systems; specific accuracy metrics not published
Privacy
How your data is protected
Retention:Building operational data retained per project lifecycle; duration not publicly specified
Training:Not publicly specified whether operational building data is used for model training
Compliance:GDPR compliant (stated in privacy policy), NIST cybersecurity framework referenced, No published SOC 2 report found
API
Integration
Endpoint:Quantum API (details available for qualified partners)
Method:Contact vendor for full API documentation

Use Cases

  • ·Autonomous HVAC control optimization for energy efficiency and comfort
  • ·Real-time building commissioning and fault detection
  • ·Multi-building portfolio management with ESG/EPBD compliance reporting
  • ·Predictive maintenance and equipment lifecycle management
  • ·Carbon reduction and energy monitoring for sustainability goals
  • ·Occupant comfort optimization with autonomous thermal control
  • ·Edge computing for critical infrastructure with minimal latency

Pricing

Free
Not publicly available
Pro
Contact vendor for SaaS platform pricing (hardware + software model)
Enterprise
Custom pricing based on building count, complexity, and integration scope
Sources & Research NotesResearch date: 2026-02-01
Fields Checked (17)
vendor, website, fundingTotal, yearFounded, maturityStage, customerBase, problem, solution, deliveryMethod, disciplines, projectPhases, projectTypes, algorithm-approach, control-features, use-cases, privacy-gdpr-compliance, hardware-specifications
Not Found (7)
api-endpoint-full-documentation, pricing-public-tiers, soc2-certification, data-training-policy-details, algorithm-accuracy-published-metrics, specific-customer-count, webhook-support
About this research: This record summarizes the cited sources and fields checked. A research date does not establish hands-on testing. Consult the original sources for current details and limitations.