Black Capital Technology partnered with Institut for Fugtteknik to architect and build the MoistLab Platform - transforming expert moisture diagnostics into a standardized, scalable digital system.

The scope included:

• Platform and system architecture
• Data pipeline and governance model
• Integration of multi-modal sensor data
• AI model integration and interpretation logic
• Automated, standardized report generation
• Interactive UI for diagnostics and insights
• Foundation for future 3D / AR visualization

Moisture diagnostics in buildings have historically relied on rudimentary tools and expert judgement — often leading to misdiagnosis and costly remediation.

MoistLab combines multi-modal measurements with AI-based interpretation to deliver accurate, repeatable and explainable diagnostics.

The platform:

• Ingests weight-percent moisture data, thermal imagery and salt analyses
• Applies AI models to classify moisture type and likely source
• Automatically generates standardized diagnostic reports
• Provides an interactive interface for exploring building insights
• Is designed to expand into 3D / AR visualization for clearer communication
• The goal was not to replace experts - but to reinforce expert judgement with data-driven certainty.

The process focused on aligning expert-level diagnostics with stakeholder clarity.

BCT worked closely with Institut for Fugtteknik (and research partners) to:

• Define a robust data structure before applying AI
• Ensure traceability from raw measurement to final diagnosis
• Govern workflows so results could be trusted by both specialists and non-experts
• Design outputs that engineers, architects and property owners could understand

This ensured the platform could move from research proof-of-concept to a real, usable product.

Image source: Alexandra Institute

Image source: EHHS

The MoistLab Platform delivers a step-change in diagnostic quality and consistency.

Key outcomes: