Three stages: first, converge every available source into a verified model of the building. Then simulate how each component is actually aging under its real environment — the asset-level physical risk that regional models can't see. Finally, turn that condition map into decisions: what to convert, recover and reuse — and how to price the risk.
We ingest every source we can find on the building — drawings, records, imagery, surveys — and converge them into one verified BIM model that labels every column, beam and wall.
Across past projects, precise as-built BIM models — every column, beam and wall labelled by hand — give the system its ground truth.
Each reconstructed model is matched to its verified BIM twin, producing thousands of supervised examples.
A model is trained on many pairs to learn how raw inputs map to real structural elements.
The system reads a new scan and resolves it into concrete columns, steel beams, brick walls — element by element.
Working system, improving over time. Stage 1's output is not 100% precise on day one. Every new building feeds back into the training set — the model gets sharper with each project.
The labelled element map produced by Stage 1 — every column, beam and wall, with its material.
Layer in humidity, temperature cycles, soil and ground contamination — the conditions acting on the structure.
Define how each factor attacks each material, and how damage propagates from one component to its connected neighbours.
Build the interactions into a propagation model and run a stress simulation across the whole structure.
A per-component verdict: what's most damaged, what's most critical, what it means for conversion feasibility.
Aim: match reality. The simulation is calibrated to track the building's actual current condition as closely as possible — measured against on-site inspections wherever available.
The per-component condition verdict from Stage 2 — a score for every column, beam and wall in the building. It is the single starting point that every decision below is built on: which buildings to convert, which materials to recover, and how to price the risk.
Scan a whole stock — say 100,000 buildings — and rank them by condition to surface the ~100 best candidates: the optimal balance of minimum construction cost, maximum profit, and maximum apartments, all delivered from what already stands rather than built new.
Knowing each component's material and condition, we know which parts are no longer sound and can be removed — a verified inventory of materials available for new construction. The blocker has always been knowing what's available and where; this produces exactly that inventory.
The material inventory feeds the circular-economy sector: what can be reused, how it is reused, and how the embodied CO₂ locked into each component is recovered and kept in use — rather than re-emitted through demolition and rebuilding from scratch.
The Stage 2 condition map, turned into an asset-level physical-risk profile underwriters lack — exactly where catastrophe models calibrated on historical data go blind at the asset or postal-code level. It powers the work to identify and prioritize physical risk vulnerabilities, quantify potential losses — the resilience advisory 91% of insurers call their clearest opportunity.
From a single condition map, four decisions. Convert, recover, reuse — and underwrite. Each building's verdict feeds the circular economy and the insurers who price its risk.