Real-time analytics engine
Continuous monitoring of energy, indoor air quality, thermal performance and occupancy, with anomaly detection.
Time series · IoT · Anomaly detection
SRCULAR
ENVOLV-AI is now Syrcular. Same team, same mission, new name.
Building Brain
Ask your building what is happening, and what to do next
Facility, sustainability and asset teams get one view of energy, comfort, equipment and carbon, plus an assistant that answers in plain language and shows the data behind every answer.
- Works with existing sensors, BMS and BIM/IFC models
- Source traceability and expert review built in
- Carbon, ESG and EU taxonomy reporting generated automatically
Pilot building A · OverviewIllustrative interface
Energy today1,284 kWh
Indoor CO₂612 ppm
Embodied carbon342 t
Heating demand, last 14 days
Why did heating demand jump on Tuesday?
An anomaly in the AHU-2 supply temperature coincides with the spike. Suggested check: valve actuator. Sources: 3 sensor series, maintenance log
Core modules
Six modules, one intelligent platform
Language-model assistant
Query performance in plain language, get recommendations and generate sustainability reports, with sources cited.
RAG · Open and commercial LLMs
Digital twin engine
Live replicas that simulate energy flows, material degradation and renovation scenarios before anything is built.
BIM/IFC · Simulation · 3D viewer
Material passport registry
Track every component from sourcing to end of life, with reuse potential and full traceability.
Circularity · LCA · Traceability
Sustainability KPI dashboard
Carbon footprint, energy performance, EU taxonomy alignment and ESG metrics, with customisable reports.
EU taxonomy · ESG · Carbon
Predictive optimisation
Forecast demand, anticipate maintenance and rank retrofit strategies by cost and carbon.
ML forecasting · Optimisation · Retrofit
Architecture
Modular by design, open by default
Each layer can be deployed on its own and integrates with the systems you already run.
Presentation
DashboardsAssistant chat3D twin viewerMobile
Intelligence
LLM enginePredictive analyticsAnomaly detectionRecommendations
Data
Time-series storeBuilding information modelMaterial passportsSensor pipeline
Integration
IoT gatewayBIM/IFC importWeather dataEnergy grid APIs
LCI-Pilot · Environmental impact
Life Cycle Inventory, drafted by AI and signed off by experts
Developed in AMALTEA with façade manufacturers, LCI-Pilot reads technical documents and drafts inventory and cost data aligned with ISO 14040/14044 and EN 15804.
- 01UploadDatasheets, bills of materials, supplier declarations
- 02ExtractAI drafts flows, quantities and costs with source links
- 03ReviewAn LCA expert checks, corrects and approves
- 04ExportInventory ready for LCA tools and EPD preparation
AMALTEA is funded by the European Union under Horizon Europe, GA 101189678. Views expressed are those of the author(s) only.Use cases
What teams do with it
Energy optimisation
ML-driven HVAC scheduling, lighting control and demand prediction.
Predictive maintenance
Spot equipment degradation early through sensor fusion and anomaly detection.
Renovation planning
Simulate retrofits to balance cost, carbon and occupant comfort.
Circular material tracking
Know the composition, reuse potential and end-of-life path of every component.