Extract the information users ask for
Translate model questions into repeatable analysis that returns elements, properties, quantities, and relationships.
Create AI-assisted IFC extraction, analysis, validation, and editing workflows that return structured, actionable model data.
Translate model questions into repeatable analysis that returns elements, properties, quantities, and relationships.
const workflow = await flinker.create({
standard: 'IFC',
host: 'Microsoft',
dataBoundary: 'customer-tenant'
});
workflow.on('model:selected', syncBusinessData); Translate model questions into repeatable analysis that returns elements, properties, quantities, and relationships.
Diagnose schema, structure, and information-quality issues before model data moves downstream.
Bring results into Excel, Power BI, portals, and Microsoft processes rather than isolating them in a chat.
Use natural-language instructions for quantity takeoff, property extraction, model analysis, compliance checks, reporting, and data migration workflows.
Ask for quantities, areas, volumes, counts, properties, and model relationships without requiring users to navigate the IFC schema manually.
Use JSON outputs in reports, portals, spreadsheets, workflow automation, or downstream integration code.
Submit work asynchronously for large models and connect completion through webhook notifications.
Commercial terms depend on the processing workflow, expected model volume, hosting boundary, support level, and application distribution model.
Product and enterprise integrations
Commercial and technical information reviewed 26 August 2026.
Talk with Flinker about architecture, deployment, commercial options, and the first production use case.