Real projects. Measurable results.
From enterprise digital twins to published research — a selection of projects that demonstrate what AI-powered building intelligence can do.
5
Projects
2
Client Deployments
2
Publications
Enterprise-Scale Digital Twin Platform
Fortune 500 Construction Company
Challenge
A Fortune 500 construction company needed real-time spatial analytics and digital twin capabilities across large-scale projects. Existing tools couldn't handle the data volume or complexity.
Solution
Built a scalable digital twin platform integrating IFC processing, spatial analytics, and real-time IoT data streams with a custom WebGL visualization layer.
- Scalable IFC processing pipeline
- Real-time spatial analytics engine
- Custom WebGL visualization layer
- Integration with IoT sensors and BMS
“The digital twin platform has revolutionized how we manage and analyze our construction data.”
— VP of Innovation, Construction Technologies
2.5M m²
Building Space Analyzed
100+
Concurrent Users
40% faster
Clash Detection Speed
300%
ROI in Year 1
Government BIM Validation Platform
Government Infrastructure Division
Challenge
Manual BIM compliance validation for public infrastructure projects was taking weeks per submission and producing inconsistent results across reviewers.
Solution
Deployed an automated IFC validation platform using custom rule engines, spatial graph analytics, and natural language query interface for non-technical reviewers.
- Custom IFC validation rules using IfcOpenShell
- Spatial graph analytics with Neo4j
- Azure OpenAI integration for NL queries
- Automated reporting and visualization dashboard
“Intento Labs transformed our BIM validation process from weeks to hours while significantly improving accuracy.”
— Senior Director, Digital Infrastructure Division
Weeks → Hours
Validation Time
88%
Accuracy
5× faster
Reviewer Efficiency
NBC + OBC
Standards Covered
Automated Space-Based Graph Generation for Building Energy Estimation
Research Publication
Challenge
Employing BIM data for Building Energy Consumption Estimation (BECE) is challenging due to complex IFC data models and incompatibility with data-driven ML algorithms.
Solution
A framework that extracts semantic, geometry, and topology information from IFC space schemas using geo-computation algorithms, making the data compatible with graph-based machine learning.
- IFC space schema parsing and enrichment
- Geo-computation for adjacency detection
- Graph construction from building topology
- Graph-based ML for energy estimation
90%+
Geometry Accuracy
100%
Adjacency Detection
Graph-ML Ready
Data Format
MDPI Buildings
Published
IFC to IDF Energy Processing & Carbon Footprint Engine
In Development
Challenge
Building energy simulation requires converting rich IFC models into EnergyPlus IDF format — a process that today is largely manual, error-prone, and disconnected from real-world climate data. Carbon footprint calculation requires integrating EPW weather files with the building's thermal model, but no automated end-to-end pipeline exists.
Solution
An AI-powered pipeline that parses IFC models, extracts thermal zones, materials, and HVAC systems, auto-generates IDF simulation files, and ingests EPW climate data to compute per-zone energy consumption and operational carbon footprint — all in one workflow.
- Automated IFC → IDF conversion with IfcOpenShell + EnergyPlus
- EPW weather file parsing and climate zone mapping
- Thermal zone extraction from IFC space schemas
- AI-assisted material property mapping to EnergyPlus datasets
- Per-zone carbon intensity calculation (kg CO₂e/m²)
- Scenario comparison: baseline vs. retrofit vs. passive design
Hours → Minutes
Conversion Time
EPW Global
Climate Datasets
kg CO₂e/m²
Carbon Metrics
EnergyPlus
Simulation Engine
Graph-Based Learning Using BIM for Energy Consumption Estimation
Research Publication
Challenge
BIM data is severely underutilized for data-driven energy approaches due to the complexity of IFC formats and their incompatibility with standard ML pipelines.
Solution
Demonstrated graph-based learning algorithms using enriched semantic, geometry, and topology information extracted from BIM for critical zone detection in energy analysis.
- BIM semantic enrichment pipeline
- Graph neural network architecture
- Critical zone detection model
- Pre/post-construction performance comparison
Validated
Critical Zone Detection
Graph-ML ready
BIM Compatibility
Pre + Post Build
Scope
TCRC 2022
Published
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