BIM checks,
on every model drop.

BIM AI for quality checking means testing every model issued to the common data environment against the project’s BIM execution plan and information requirements (naming, classification, required properties, level of information need) and returning each failure as an issue the model author can open at the element.

Rule-based checkers such as Solibri, and clash detection in Navisworks or Revizto, already do the geometric part well. What tends to go unchecked is the information: the exchange information requirements written under ISO 19650 as a PDF, which nobody has turned into rules, and properties whose values are present but wrong. That is where a model helps. It reads the EIR and BEP into explicit, testable rules, and it can spot values that are syntactically fine and semantically odd, such as a fire rating on an internal partition that contradicts the fire strategy.

The checks run on IFC, so they work across Revit, Archicad, Tekla and the rest of a federated team, and the results go back as BCF so nobody learns a new tool.

Rules extracted from an EIR are reviewed by your information manager before they run, line by line, because a misread requirement would produce confident, wrong findings at scale. After that we measure on real model drops from a past project: the issues your team raised manually should all appear, and the new ones should be ones your information manager agrees are real.

Findings feed the documents too. If the model and the specification disagree, the same check feeds specification writing, and on site the contractor’s RFIs and submittals are fewer when the model was right at issue.